Fleet Utilization Efficiency for On-Demand Transportation Services

ABSTRACT

An on-demand transportation management system can collect vehicle fleet utilization data corresponding to human-driven vehicles (HDVs) and autonomous vehicles (AVs) operating within a given region in connection with an on-demand transportation service. The on-demand transportation management system can then establish a set of selection priorities for respective areas of the given region based on the vehicle fleet utilization data, each selection priority indicating whether a respective area of the given region is to favor HDVs or AVs for servicing transport requests.

BACKGROUND

The path to autonomous vehicle (AV) ubiquity on public roads andhighways has been highly experimental across several entity types, suchas educational institutions, automobile manufacturers, and hightechnology business entities. AV testing is currently converging uponnecessary hardware—such as sensor and computational resources, requiredfor adequate safety of AV operations on public roads—as well ascontinuously advancing software development in areas of perception,object classification, path prediction, control input responses (e.g.,steering, braking, and acceleration inputs), and the like. However,monetization of AV technology has been limited to a gradual progressionof autonomy features on offered vehicles manufactured by certainautomakers—from active cruise control features to lane-keeping,following, and automated parking and braking features developed bycertain vehicle manufacturers.

In the year 2016, human deaths attributed to motor vehicles in theUnited States reached 40,000 mainly due to speeding, impaired driving,and increasingly distracted driving. It is widely accepted within theautomotive and scientific communities that advanced driver-assistancesystems and autonomous driving will tremendously reduce vehicle-relatedaccidents and deaths. In addition, wasted time and productivity costsattributed to lengthy commutes may also be significantly reduced orlargely eliminated once self-driving vehicle technology becomesubiquitous in urban sprawls. However, widespread acceptance ofautonomous vehicles can only be achieved through proven, real-worldresults in terms of logged mileage and an indisputable and convincingsafety track record.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure herein is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements, and in which:

FIG. 1A is an example road network map including a mapped and labeledautonomy grid on which AVs can operate;

FIG. 1B shows an example of an autonomously controlled self-drivingvehicle utilizing sensor data and localization maps to navigate a roadsegment of an autonomy grid, in accordance with example implementations;

FIG. 2 is a block diagram illustrating an example AV software trainingsystem utilized in-connection with an AV fleet and an on-demandtransportation management system;

FIG. 3 is a block diagram illustrating an example on-demandtransportation management system linking available service providervehicles with requesting users within a given region;

FIG. 4 is a block diagram illustrating an example on-trip monitoringsystem utilized in-connection with an on-demand transportationmanagement system;

FIG. 5 is a block diagram illustrating an example autonomous vehicle incommunication with on-demand transportation management systems, asdescribed herein;

FIG. 6 is a block diagram illustrating an example driver device utilizedby human drivers in-connection with an on-demand transportationmanagement system;

FIG. 7 is a flow chart describing example methods of generalizingfractional harmful or risky events for path segments of a given region;

FIG. 8 is a flow chart describing example methods of matching atransport request with a service provider vehicle using risk regressionand trip classification methods described herein;

FIG. 9 is a flow chart describing example methods of simulation-basedprecertification of AV software;

FIG. 10A is a flow chart describing example methods of dynamic softwareversion and/or autonomy mode switching;

FIG. 10B is a flow chart describing example methods of post-trip AVmanagement;

FIG. 11 is a flow chart describing example methods of evaluating AVsoftware releases against human and/or AV driving data;

FIG. 12 is a flow chart describing example methods of software releaseverification for execution by fully autonomous self-driving vehicles;

FIG. 13 is a flow chart describing example methods of individualizedrisk regression-based vehicle matching by an on-demand transportationmanagement system;

FIG. 14 is a flow chart describing example methods of intelligentrouting of human drivers using fractional risk techniques describedthroughout the present disclosure;

FIG. 15 is a flow chart describing example methods of individualizedrouting, according to various examples;

FIG. 16 is a flow chart describing example methods of vehicle matchingbased on non-trip risk, according to examples;

FIG. 17 is a flow chart describing example methods of efficient fleetutilization in connection with an on-demand transport service, accordingto examples described herein;

FIG. 18 is a hardware diagram illustrating an example computer systemfor AVs upon which examples described herein may be implemented; and

FIG. 19 is a hardware diagram illustrating a computer system upon whichexample backend software training, on-demand transport management, andon-trip monitoring systems described herein may be implemented.

DETAILED DESCRIPTION

A travel-path network for a given region (e.g., a road network for ametroplex such as the greater Pittsburgh, Pa. metropolitan area) can beanalyzed on a high-level and mapped using ground truth data fromrecording vehicles having sensor systems (e.g., LIDAR and stereoscopiccameras). The travel-path network can then be parsed intocapability-in-scope lanes and paths across the region for potential AVoperation. These capability-in-scope path segments can be determined ona high level based on lane geometry, intersection complexity, trafficlaw complexity, traffic flow, pedestrian density, and the like.Furthermore, the capability-in-scope path segments can be temporallysensitive and conditionally sensitive. For example, certain pathsegments can be safe for AV operation only during certain times of dayor night, when traffic conditions permit, or when weather conditionspermit. The capability-in-scope paths can be determined initially byhumans, and then refined and/or expanded computationally through riskregression methods, trip classification methods, and AV softwareenhancement described throughout the present disclosure.

Additionally or alternatively, the capability-in-scope lanes and pathscan be determined through ground truth mapping and labeling, andheuristically through lower level computational analysis of AV log datafrom AVs traveling throughout the region. As an example, log-sets fromAVs can be processed by a trained risk regressor to determine afractional risk quantity for an AV operating on any given path segment,and higher risk path segments may be eliminated or set aside for futuresoftware development and eventual expansion using the disclosed methodsherein. Accordingly, the resultant capability-in-scope lanes and pathscan comprise an autonomy grid of highly mapped and labeled paths (e.g.,recorded and labeled localization maps on an individual lane basis) thatprovide low predicted risk for AV operation.

As provided herein, a “path segment” can comprise a paved road segment,unpaved road segment, or off-road segment utilizable by vehicles, andcan include predetermined paths over land, water, underwater, andthrough the air (e.g., for aerial drones used for autonomous package orhuman transport). Thus, a “path” can comprise any sequence of connectedpath segments traversable by a vehicle, and can further comprise anycombination of land, aerial, and aquatic path segments. Along theselines, a “driver” may be any operator of a vehicle, such as an aerialvehicle, a typical road-based or off-road vehicle, a marine vehicle, ora hybrid aerial, land, and/or marine-based vehicle. Furthermore, a “lanesegment” included on a typical paved road in a road network can have apredetermined length (e.g., two-hundred meters) and/or can be parsedfrom a road segment between intersections. A “road segment” can becomprised as multiple individual lane segments (e.g., a left lanesegment and a right lane segment) having a common directional aspect.Accordingly, a total path for an AV from a starting point to adestination can be comprised of a sequential set of capability-in-scopelane segments from the starting point to the destination, each having anattributed fractional risk quantity calculated by a risk regressor basedon static and dynamic conditions, as described herein.

Described throughout the present disclosure are risk regression and tripclassification techniques between human-only driven vehicles (HDVs), AVshaving a safety driver (SDAVs), fully autonomous self-driving vehicleswith no safety driver needed (FAVs) (e.g., level 4 or level 5 autonomy),or remotely operated autonomous vehicles. Further described throughoutthe present disclosure is an on-demand transportation management systemthat manages on-demand transportation services linking available driversof purely human-driven vehicles (HDVs), available autonomous vehicleshaving trained safety drivers (SDAVs), and fully autonomous self-drivingvehicles having no safety driver (FAVs) with requesting ridersthroughout the given region.

In doing so, the on-demand transport management system (or “transportsystem”) can receive requests for transportation from requesting usersvia a designated rider application executing on the users' computingdevices. On a high level, the transport management system can receive atransport request and identify a number of proximate available vehiclesrelative to the user. The transport system may then select an availableHDV, SDAV, or FAV to service the transport request based on a number ofcriteria described herein, including risk, estimated time of arrival(ETA) to the pick-up location, expected earnings or profit per candidatevehicle, and the like. As provided herein, examples of the transportrequest can comprise an on-demand carpooling request, a standardride-share request, a high-capacity vehicle request, a luxury vehiclerequest, a professional driver request, a request for AV transportation,a request for item delivery (e.g., a package or food), or anycombination of the foregoing. A transport request may also include anygeneral request for transportation that does not necessarily specify thetype or category of transportation used to fulfill the request.

According to examples described herein, the transport system can performa vehicle matching operation given a received transport request from arequesting user. The matching operation can comprise identifying a setof candidate vehicles based on risk regression techniques, tripclassification techniques, business optimization techniques, and variousother parameters described herein, and ultimately selecting a mostoptimal vehicle to service the transport request. In doing so, thetransport system can select between HDVs, SDAVs, and/or FAVs totransport a requesting user from the pick-up location to a destinationindicated in the transport request. Examples described herein canleverage the use of SDAVs for software release testing and verificationfor eventual, post-verification use on FAVs to progress the transitiontowards FAV ubiquity for on-demand transportation services.

As described herein, a “risk regressor” or “risk regression engine” maybe used interchangeably throughout to describe machine learningtechniques and/or algorithms to compute fractional risk quantities forany given path segment of a given region (e.g., a certain probabilitythat a harmful event will occur for any given traversal of a specifiedlane of a road segment between intersections). Furthermore, an examplerisk regressor may further factor in current environmental conditions(e.g., rain, snow, clouds, road conditions, lighting, lightingdirection, and the like), and static risk based on lane geometry,traffic conditions, and time of day to compute a fractional riskquantity dynamically for any path segment at any given time. Suchfractional risk quantities can be generalized for human driving, or canbe AV and/or AV software version specific.

Accordingly, an example risk regressor may compute an individualizedfractional risk quantity for each path segment on a per vehicle or perdriver basis given the vehicle's or driver's attributes, such ason-board hardware and software, an AV state as determined throughvehicle telemetry or diagnostics data, or a driver's safety history,current state, and driving characteristics. In some examples, aparticular risk regressor may be trained for a corresponding tripclassifier, and may be specific to a software release executable by AVsfor verification, as described herein. In addition, for a giventransport request from a requesting user, a routing engine can determinea set of routes between the pick-up location and destination, and therisk regressor can determine an aggregate risk quantity for each ofthose routes given the current or predicted conditions (e.g., conditionsat the time the vehicle traverses a particular path segment), andprovide a lowest risk route or other optimal route (e.g., optimizedacross risk, time, dollar earnings, etc.) as output to a trip classifierand/or vehicle matching engine that ultimately pairs the requesting userwith an available vehicle.

As provided herein, a “trip classifier” or “trip classification engine”may be used interchangeably throughout the present disclosure todescribe machine learning techniques and/or algorithms thatclassify—based on an aggregate risk quantity estimated or otherwisecalculated by a risk regressor—any overall path or route between aninitial location and a destination. For example, a trip classifier canreceive, as input, one or more routes and aggregate risk quantities foreach of the routes (as determined by a risk regressor). In variations,further inputs can be provided to the trip classifier, such as expectedearnings or profitability for a particular vehicle class by servicingthe trip given current conditions. A classification by a trip classifiercan include multiple elements, such as specific software versionsauthorized for a trip along the entirety of the route (e.g., verifiedsoftware releases or new, unverified software releases stored on-boardAVs), the type of vehicles authorized to service the trip (e.g., SDAV,FAV, and/or HDV), and the like. Accordingly, based on the aggregate riskof a total path or route, the trip classifier can establish a set ofthreshold requirements on vehicles for servicing a particular transportrequest for the trip, and therefore can ultimately determine a candidateset of vehicles that are qualified to service the transport request.Thereafter, a vehicle matching engine can select a most optimal vehicleto service the request.

As provided herein, a “software release” or “software version” comprisesany software update executable by an AV for virtually any reason.Example reasons for a new software release can include hardware updateson AVs, altering perception, prediction, or vehicle control behavior bythe AV, expanding AV operations on an autonomy grid, providing new orupdated localization maps to the AVs, and the like. In various examples,each software release may be paired with a particular trip classifier(e.g., a trip classifier and software release pair can be lockedtogether without being used for other software releases and tripclassifiers). Furthermore, when a software release is developed, it mayfirst be pre-certified through a set of simulations, such as full logset simulations developed through previously verified software releasesand log data from actual AV trips, edge case Monte Carlo sampling,plan-based evaluation, and simulation analysis capable of adjustingsimulation parameters (e.g., simulating fault conditions and failures)and incorporating additional actors (e.g., other vehicles orpedestrians) to provide a broad range of test scenarios with highlygranular control. In some aspects, a new software release can beverified for FAV use using simulations (e.g., a minor update to AVbehavior at a specified location, such as a blind corner). In otheraspects, pre-certification using simulation analysis enables softwaretraining systems described herein to distribute pre-certified softwarereleases to SDAVs for logging mileage and eventual verification.Furthermore, beyond simulation, examples described herein can alsoleverage recorded log data from AVs executing software while being runthrough a set of scenarios and/or tests in a controlled trackenvironment.

Verification of a software release corresponds to authorization of thatsoftware release for use on FAVs without the need for a safety driver.Thus, as described herein, FAVs would only operate using verifiedsoftware versions, with certain limited exceptions described herein. Asoftware release is verified when a certain confidence level is met(e.g., a 95% confidence that the software release is safer than anaverage human driver over a certain collection of road miles overnominally equivalent driving conditions). Furthermore, thresholds toachieve verification can be determined or adjusted based on simulationresults for pre-certification and/or real-world testing, as describedherein. In various examples, a given software release may be AV testedover a variety of driving conditions, or a defined set of conditions(e.g., on test tracks).

Examples described herein may reference software training techniquesthat correspond to machine learning, neural networks, artificialintelligence, and the like. Certain examples provided herein describetraining a new risk regressor or trip classifier. Such training cancorrespond to supervised or unsupervised machine learning methods toaccurately quantify fractional risk for traversing any given pathsegment based on historical harmful event data and/or close call datafrom AV logs and other sensor systems (e.g., driver computing devices).Such training can further correspond to supervised or unsupervisedmachine learning methods to accurately classify a given route—based onan aggregate risk quantity for the route—to determine which vehicletypes are capable of servicing the route (e.g., between HDVs, SDAVs, andFAVs) and which software versions are certified for execution to servicethe route.

Among other benefits, the examples described herein achieve a technicaleffect of safely expanding autonomous vehicle operations through dynamicrisk analysis, trip classification, and robust software verification.According to various examples described herein, the on-demandtransportation management system can operate in connection with an AVsoftware training system and on-trip monitoring system to provideon-demand transportation services to requesting users with an objectiveto perform comprehensive AV software evaluation with high verificationstandards for execution by FAVs. The multi-pronged approaches describedthroughout the present disclosure provide beneficial linkages between anexisting on-demand transportation service platform involving HDVs,extending that platform to SDAVs, leveraging the safety driver aspect ofthe SDAVs for AV software testing and verification, and deploying afleet of FAVs utilizing verified AV software.

As used herein, a computing device refers to devices corresponding todesktop computers, cellular devices or smartphones, personal digitalassistants (PDAs), laptop computers, tablet devices, virtual reality(VR) and/or augmented reality (AR) devices, wearable computing devices,television (IP Television), etc., that can provide network connectivityand processing resources for communicating with the system over anetwork. A computing device can also correspond to custom hardware,in-vehicle devices, or on-board computers, etc. The computing device canalso operate a designated application configured to communicate with thenetwork service.

One or more examples described herein provide that methods, techniques,and actions performed by a computing device are performedprogrammatically, or as a computer-implemented method. Programmatically,as used herein, means through the use of code or computer-executableinstructions. These instructions can be stored in one or more memoryresources of the computing device. A programmatically performed step mayor may not be automatic.

One or more examples described herein can be implemented usingprogrammatic modules, engines, or components. A programmatic module,engine, or component can include a program, a sub-routine, a portion ofa program, or a software component or a hardware component capable ofperforming one or more stated tasks or functions. As used herein, amodule or component can exist on a hardware component independently ofother modules or components. Alternatively, a module or component can bea shared element or process of other modules, programs or machines.

Some examples described herein can generally require the use ofcomputing devices, including processing and memory resources. Forexample, one or more examples described herein may be implemented, inwhole or in part, on computing devices such as servers, desktopcomputers, cellular or smartphones, personal digital assistants (e.g.,PDAs), laptop computers, virtual reality (VR) or augmented reality (AR)computers, network equipment (e.g., routers) and tablet devices. Memory,processing, and network resources may all be used in connection with theestablishment, use, or performance of any example described herein(including with the performance of any method or with the implementationof any system).

Furthermore, one or more examples described herein may be implementedthrough the use of instructions that are executable by one or moreprocessors. These instructions may be carried on a non-transitorycomputer-readable medium. Machines shown or described with figures belowprovide examples of processing resources and computer-readable mediumson which instructions for implementing examples disclosed herein can becarried and/or executed. In particular, the numerous machines shown withexamples of the invention include processors and various forms of memoryfor holding data and instructions. Examples of non-transitorycomputer-readable mediums include permanent memory storage devices, suchas hard drives on personal computers or servers. Other examples ofcomputer storage mediums include portable storage units, such as CD orDVD units, flash memory (such as those carried on smartphones,multifunctional devices or tablets), and magnetic memory. Computers,terminals, network enabled devices (e.g., mobile devices, such as cellphones) are all examples of machines and devices that utilizeprocessors, memory, and instructions stored on computer-readablemediums. Additionally, examples may be implemented in the form ofcomputer-programs, or a computer usable carrier medium capable ofcarrying such a program.

As provided herein, the term “autonomous vehicle” (AV) describes anyvehicle operating in a state of autonomous control with respect toacceleration, steering, braking, auxiliary controls (e.g., lights anddirectional signaling), and the like. Different levels of autonomy mayexist with respect to AVs. For example, some vehicles may enableautonomous control in limited scenarios, such as on highways. Moreadvanced AVs, such as those described herein, can operate in a varietyof traffic environments without any human assistance. Accordingly, an“AV control system” can process sensor data from the AV's sensor array,and modulate acceleration, steering, and braking inputs to safely drivethe AV along a given route.

Autonomy Grid

FIG. 1A is an example road network map including a high level mapped andlabeled autonomy grid on which AVs can operate. The road network map 100can identify all roads and paths of a given region (e.g., a metropolitanarea), and further indicate the individual lanes of each road on a lowerlevel. The autonomy grid 105 shown in FIG. 1A represents a current,limited road network on which AVs can operate, and can comprise entireroad surfaces, or can be lane-specific (e.g., only right lanes forcertain road segments). Furthermore, with added ground mapping and/ortesting, the autonomy grid 105 can be expanded gradually with an overallgoal of encompassing the whole road network of the road network map 100.For example, localization maps can be recorded and processed to expandcertain segments of the autonomy grid 105 as AV hardware and softwarebecome more robust and capable.

Autonomous Vehicle in Operation

FIG. 1B shows an example of an autonomously controlled self-drivingvehicle utilizing sensor data and localization maps to navigate a roadsegment of an autonomy grid, in accordance with example implementations.In an example of FIG. 1B, the autonomous vehicle (AV) 110 may includevarious sensors, such as a roof-top camera array (RTC) 122,forward-facing cameras 124 and laser rangefinders 130 (e.g., LIDARsystems). As provided herein, the AV 110 can comprise an SDAV having asafety driver that can take over manual control, or can comprise a FAVhaving no safety driver or manual control capabilities. In certainaspects, an FAV may be manually overridden remotely (e.g., by a remoteassistance operator).

In some aspects, a data processing system 125, comprising a computerstack that includes a combination of one or more processors, FPGAs,and/or memory units, can be positioned in the cargo space of the AV 110.The data processing system 125 can store a set of localization maps, orsubmaps having labeled static ground truth data, that the AV 110references when traverse sequential path segments to dynamically comparewith a live sensor view 113 of the AV 110 to detect and classify dynamicobjects, such as pedestrians 114 or other vehicles 127. Examples oflabeled static objects can include parking meters 127, traffic signals140, crosswalks 115 and/or sidewalks 121.

According to an example, the AV 110 processes the live sensor view 113(e.g., a stereoscopic or three-dimensional LIDAR image of theenvironment 109) to scan a current path segment 133 on which the AV 110traverses. The AV 110 can process image data or sensor data,corresponding to the sensor view 113 from a set of on-board sensorsin-order to detect dynamic objects that are, or may potentially be, inthe path of the AV 110. In an example shown, the dynamic objects includea pedestrian 114 and another vehicle 127—each of which may potentiallycross into a road segment along which the AV 110 traverses. The AV 110can analyze a current localization map to and/or image data from thesensor views 113 reference information about the path segment 133, suchas identifying the divider 117, the opposite lane, sidewalks 121,sidewalk structures such as parking meters 127 and road signs, trafficsignals 140, bike lanes, crosswalks 115, lane boundary markers, andlocalization markers, such as buildings, trees, and other uniquestructures.

The data processing system 125 of the AV 110 may run one or moresoftware versions to process the sensor view 113 and generate controlinputs accordingly, such as acceleration, braking, and steering inputs.The sensor view 113 may comprise three-dimensional sensor images thatcombine sensor data from the roof-top camera array 122, front-facingcameras 124, and/or laser rangefinders 130 (e.g., LIDAR sensors).Certain software versions may be fully verified for safe and reliableuse by fully autonomous vehicles, such as vehicles having level 4 orlevel 5 autonomy. Other software versions can be executed by the AV 110in limited circumstances, or can have a verification in-progress statuswhile the AV 110 logs verification mileage using the new softwareversion. As described in detail below, the AV 110 can benetwork-connected, and can communicate with a backend, on-demandtransportation management system that can transmit routing instructionsto the AV 110 in-connection with an on-demand transportation service,such as package delivery or human transport. In certain implementations,the AV 110 may be instructed to switch between software versions betweentrips or dynamically in accordance with on-trip monitoring systemdescribed herein.

The AV 110 shown in FIG. 1B may comprise a safety-driven autonomousvehicle (SDAV) having a dedicated human safety driver ready to take overmanual control of the AV 110, or a fully autonomous vehicle (FAV)capable of autonomous operation without a safety driver. For SDAVimplementations, the AV 110 can operate in an autonomous mode in whichthe data processing system 125 takes over control of the AV's controlmechanisms, and a manual mode in which the safety driver takes overcontrol. In some aspects, the safety driver can take over controltemporarily to make a swift correction, such as braking for a partiallyhidden stop sign or accelerating and steering if the AV 110 is in astuck state. For FAV implementations, the AV 100 need not include driverfeatures, such as accelerator and brake pedals, or even a steeringwheel.

System Descriptions

FIG. 2 is a block diagram illustrating an example AV software managementsystem utilized in-connection with an AV fleet and an on-demandtransportation management system. In the below discussions of FIGS. 2through 6, reference is made to logical blocks representing thefunctional aspects of software, hardware, or a combination of softwareexecuting on hardware, such as a remote datacenter. In various examples,the AV software management system 200 may be used to, for example, traina new risk regressor 230, train a new trip classifier 250, or verify anew software version 252 being run on SDAVs 281 of an AV fleet 285. TheAV software management system 200 can comprise a database 240 storingtrip log data 242, historical event data 244, and software version logs246 that include verified software versions 251 and new, or in-progress,software versions 242. As an example, AV software engineers can developnew software versions 252 for execution by the AV fleet 285. The AVsoftware management system 200 can include a software verificationengine 220, which can distribute the new software versions 252 to SDAVs281 and/or FAVs 289 operating within a given region. In variousexamples, the verification engine 220 can further distribute verifiedsoftware versions 251 to the SDAVs 281 and/or FAVs 289 throughout thegiven region.

According to various examples, the AV software management system 200 caninclude an AV interface 215 that connects the AV software managementsystem 200 to one or more networks 280. Accordingly, the AV softwaremanagement system 200 can remotely communicate with the SDAVs 281 andthe FAVs 289 operating throughout the given region. For example, theverification engine 220 can distribute the new software versions 252 andthe verified software versions 251 to the SDAVs 281 and FAVs 289 overthe one or more networks 280. Furthermore, the AV interface 215 canreceive AV location data 288 and AV log data 291 from each of the SDAVs281 and FAVs 289.

A new or test software version 252 can comprise an update to any mannerin which an AV operates. The new software versions 252 can includeupdates to the manner in which the AV interprets or responds to sensordata (e.g., perception or object prediction updates), can correspond tohardware updates and/or sensor configurations on the AV, or cancorrespond to localization map updates. In one example, a new softwareversion 252 that simplifies sensor data processing, requiring lesscomputing power, can be distributed to SDAVs 281 to determine whetherthe new software version 252 is safe and reliable enough for normal useon FAVs 289. As another example, certain SDAVs 281 can operate withstreamlined hardware configurations (e.g., less sensor equipment and/orless computational hardware). A new software version 252 can beconfigured for AV operation using the streamlined hardwareconfigurations of these specific SDAVs 281. This new software version252 may then be distributed to those specific SDAVs 281 in order to logmileage in varying conditions for verification, as described herein.

In certain implementations, each new software version 252 can correspondto a specified risk regressor 230 and a specified trip classifier 250 ofthe AV software management system 200. The risk regressor 230 can betrained to aggregate fractional risk quantities across routes todetermine an aggregate risk value 232 for a particular trip. Forexample, a requesting user can make an on-demand transportation requestto transport the requesting user from a pick-up location to adestination. In various examples described throughout the presentdisclosure, the transport requests can be handled by the on-demandtransport system 201, which can determine an optimal route between thepick-up location and the destination (e.g., a shortest route in terms ofdistance or time). In some examples, the on-demand transport system 201can determine a plurality of possible routes, and the risk regressor 230can determine an aggregate risk value 232 for each of the plurality ofpossible routes.

In various implementations, the AV software management system 200 caninclude a fractional harmful event quantifier 245 that cancomputationally analyze historical event data 244 for the given region,such as vehicle incidents and collisions, to determine a fractional riskvalue 247 for each path segment of the given region. In furtherimplementations, the fractional harmful event quantifier 245 can alsoparse through trip logs 242 from the SDAVs 281 and FAVs 289 operatingthroughout the given region to identify trip anomalies, such as harmfulevents and close calls, to further factor into the fractional riskvalues 247. As provided herein, a harmful event can correspond tophysical contact between an AV and another object, such as anothervehicle, a curb, a road sign, a pedestrian, and the like. A close callcan correspond to any scenario in which a certain risk threshold hasbeen exceeded. For example, a close call can be identified as spikes inaccelerometer data in the trip logs 242, which can correspond to hardbraking events or swerving events. In other examples, close calls cancorrespond to the AV inadvertently breaching an exclusion zone, such asa crosswalk, an intersection, or getting too close to a pedestrian orother vehicle. Such close calls can be identified by the fractionalharmful event quantifier 245 in, for example, the live sensor datawithin the trip logs 242.

Accordingly, for each path segment of the autonomy grid 105, thefractional harmful event quantifier 245 can compute a fractional riskvalue 247 for traversing the path segment. As provided herein, thefractional risk values 247 can be specific to AVs or generalized for allvehicles operating within the autonomy grid 105. Additionally oralternatively, the fractional risk values 247 can be specific to aparticular software version (e.g., a new software version 252 orverified software version 251) executing on the SDAVs 281 and FAVs 289.Additionally or alternatively still, the fractional risk values 247 maybe condition-specific. For example, each harmful event or close call canbe correlated with a set of current conditions at the time of the eventor close call. This set of current conditions can include lightingconditions, weather conditions (e.g., precipitation or fog), roadconditions (e.g., wet, icy, dry, or drying), traffic conditions (e.g.,other vehicles and/or pedestrian traffic), a time of day or time ofweek, and the like. As described below, for a given trip route 252, thecurrent conditions 253 for the trip route 252 can be compared to thecondition-depend fractional risk values 247 for the risk regressor 230to ultimately determine the aggregate risk value 232 for the resultanttrip. The fractional harmful event quantifier 245 can receive dataindicating the current conditions 253 from the AV log data 291 (e.g.,sensor data showing the weather and road conditions), or any number ofthird party resources (e.g., a live weather resource, live trafficresources, etc.).

In various examples, the AV software management system 200 can include asimulation engine 260 that can run a new software version 252 through aninitial set of simulations for pre-certification of the new softwareversion 252 prior to distribution to the SDAVs 281. As provided herein,simulation-based pre-certification corresponds to either a confirmationthat the new software version 252 meets certain safety standards forexecution on SDAVs 281 and/or FAVs 289. For example, the simulationengine 260 can generate a forward simulation for a new software version252 using recorded trip logs 242 and/or simulation configurations 274,which can be configured by human engineers or automatically by theverification engine 220. In the forward simulation, the simulationengine 260 can replay any number of trip logs 242 using the new softwareversion 252 to verify that the various responses executed by the newsoftware version 252 (e.g., acceleration, braking, steering, and/orsignaling inputs) are safe enough to initiate the verification process.As such, the simulated AV—executing the new software version 252—is notconfined by the recorded trip log 242, but can rather execute its ownlow level trajectories accordingly.

In certain examples, the simulation engine 260 can further executeplan-based evaluation of the new software version 252 by confining thenew software version 252 to the recorded trip log 242 without enablingfree execution of vehicle trajectories. In further examples, thesimulation engine 260 can adjust parameters of the simulation based onthe simulation configurations 274, and can thus simulate AV failures(e.g., sensor failures or mechanical failures), sensor data occlusions,additional entities (e.g., simulated vehicles, objects, or pedestrians),and the like. The simulation engine 220 output a set of simulationresults 262 to the verification engine 220 that either pre-certifies thenew software version 252 or indicates that the new software version 252requires further refinement.

In certain aspects, when the simulation results 262 indicate that thenew software version 252 is pre-certified, the verification engine 220can generate a pre-certification trigger 224 to label the new softwareversion 252 as being certified for distribution to the SDAVs 281 and/orFAVs 289 for real-world testing and safety verification. Oncepre-certified by the simulation engine 260, the verification engine 220can distribute the new software version 252 to the SDAVs 281 and/or FAVs289, which can execute the new software version 252 selectively. In oneexample, the SDAVs 280 and/or FAVs 289 can independently begin executingthe new software version 252 throughout the autonomy grid 105. Invariations, the SDAVs 281 and/or FAVs 289 can be triggered to executethe new software version 252 via transport instructions 258 from a tripclassifier 250, as described herein.

In various implementations, the AV software management system 200 cantrain a trip classifier 250 for the new software version 252. Forexample, the trip classifier 250 can coordinate with the on-demandtransport system 201 to classify a requested trip between a pick-uplocation and a destination. In doing so, the trip classifier 250 canreceive an aggregate risk value 232 for an optimal trip route 252between the pick-up location and the destination as calculated by therisk regressor 230. As described herein, the aggregate risk value 232can account for such factors as lane geometry, path segment complexity(e.g., bicycle lanes, intersections, crosswalks, school zones, roadsignage, etc.), current environmental conditions, time of day, andtraffic conditions. Based on the aggregate risk value 232, the tripclassifier 250 can determine (i) which vehicles types may service thetransport request (i.e., SDAVs 281, FAVs 289, or HDVs), and (ii) whichsoftware version or version type is to be executed for the trip (e.g., anew versus a verified software version). As such, the trip classifier250 can operate in accordance with a set of risk thresholds thatdetermine whether the use of a particular software version 251, 252 isauthorized given the aggregate risk value 232.

Accordingly, given a transport request from a requesting user, theon-demand transport system 201 can provide the trip classifier 250 withan ideal route 252 for the trip. In variations, the on-demand transportsystem 201 can further provide a set of routes 252 for the trip. In suchvariations, the risk regressor 230 can provide an aggregate risk value232 for each of the trip routes 252, and the trip classifier 250 canclassify the trip for each of the routes 252. As an example, for a giventrip route 252, the trip classifier 250 can authorize the use of a newsoftware version 281 for execution on SDAVs 281 based on the aggregaterisk value 232. In further examples, the trip classifier 250 canauthorize the use of a set of software versions 251, 252 for a giventrip route 252. Accordingly, the output of the trip classifier 250 cancomprise a trip classification 254, which can include a set of softwareversions authorized for execution for servicing the trip. In oneexample, the trip classifier 250 can further act as a filter for anycandidate set of vehicles within a certain proximity of a requestinguser.

The trip classifier 250 may then transmit the trip classification 254 toa matching engine 255 of the on-demand transport system 201. Thematching engine 255 shown in FIG. 2 comprises a functional block of theon-demand transport management system 201, and thus is shown as a dashedblock in FIG. 2. As described in detail below, the matching engine 255can utilize the trip classification 254 to filter through a candidateset of vehicles for the transport request, and select an optimal vehicleto ultimately service the transport request. As described herein, thematching engine 255 can make the selection based on a variety offactors, including the aggregate risk value 232, estimated time torendezvous with the requesting user (e.g., based on distance andtraffic), estimated revenue for the vehicle, and the like. Accordingly,the matching engine 255 can return a trip match 256 identifying aselected vehicle to service the transport request.

Based on the trip match 256, the trip classifier 250 or the matchingengine 255 can generate a set of transport instructions 258 to transmitto the selected vehicle. If the selected vehicle is an HDV, thetransport instructions 258 can comprise an invitation to the driver toservice the transport request, as described below. However, if theselected vehicle is an SDAV 281, the transport instructions 258 caninclude routing information for rendezvousing with the requesting userand transporting the user to the destination, and one or more specificsoftware versions to execute in servicing the transport request. In oneaspect, the transport instructions 258 can parse out the trip intosegments, where the SDAV 281 is to execute a different software versionfor each segment (e.g., a verified software version 251 for a more riskysegment, and a new software version 252 for a less risky segment).

The SDAVs 281 and FAVs 289 can transmit or stream log data 291 back tothe AV software management system 200. The log data 291 can compriselive or recorded sensor data (e.g., image data, stereoscopic cameradata, LIDAR data, radar data), telemetry data (e.g., indicating thevehicle's position, orientation, velocity, current route plan, currenttrajectory, etc.), diagnostics data (e.g., indicating the vehicle's tirepressures, engine temperature, fuel or energy level, and faults orfailures in the sensor, hardware, or mechanical components of thevehicle), and/or input data indicating the AV control systemacceleration, braking, and steering input responses. The log data 291can further include correlation data indicating which softwareversion(s) were in use during operation or collection of the recorded orstreamed data.

The log data 291 can be processed by the verification engine 220 todetermine whether a new software version 252 can be verified for fullautonomous usage by the FAVs 289. In various examples, the verificationengine 220 can verify the new software versions 252 in accordance with asafety standard, which can be a regulated government standard or aproprietary standard of the on-demand transport system 201. For example,the safety standard can correspond to a confidence level (e.g., 98%certainty) that the new software version is safer than an average humandriver in a defined set of conditions. These conditions can comprisenominal conditions in terms of traffic, visibility, weather, and roadconditions. In variations, a new software version 252 may incorporatesafety updates for the AV to operate in inclement weather, at nighttime,in heavy traffic, etc. Accordingly, in order to achieve the mandatedconfidence level, the new software version 252 must be run for a certainmileage without experiencing a harmful event or close call (e.g., on theorder of millions of miles). However, as described herein,pre-certification of the new software version 252 by the simulationengine 260 may cut down on overall necessary mileage for verification.

When the AV log data 291 from the SDAVs 281 indicates that a newsoftware version 252 has logged a requisite mileage—achieving thepredetermined confidence threshold that the new software version 252meets a defined set of safety standards (e.g., 95% confidence that theAV software version 252 is safer that the average human driver)—theverification engine 220 can generate a software verification trigger 222for the new software version 252 indicating that the new softwareversion 252 is verified for execution on FAVs 289. The trigger 222 canrelabel the new software version 252 as a verified software version 251,and the verification engine 220 may then distribute the newly verifiedsoftware version 251 accordingly. For example, the verification engine220 can distribute the verified software version 251 to all FAVs 289, orcertain SDAVs 281 and/or FAVs 289 meeting a set of standardscorresponding to the verified software version 251. In certain aspects,this set of standards can comprise a set of hardware standards (e.g.,necessary sensor and/or computational equipment), and/or a set ofmechanical standards (e.g., necessary mechanical equipment, such as acertain type of tire or suspension, flight capability, float capability,submersible capability, minimum road clearance, etc.). Accordingly, theverification engine 220 can selectively distribute the verified softwareversion 252 to only those FAVs 289 and/or SDAVs 281 that meet the set ofstandards of the software version 252.

FIG. 3 is a block diagram illustrating an example on-demandtransportation management system linking available service providervehicles with requesting users within a given region. The on-demandtransport management system 300 can communicate, over one or morenetworks 390, with requesting users or riders 374 throughout a givenregion where on-demand transportation services are provided.Specifically, each requesting user 374 can execute a service application375 on the user's 374 computing device 370. As provided herein, theuser's computing device 370 can comprise a mobile computing device,personal computer, tablet computing device, virtual reality (VR) oraugmented reality (AR) headset, and the like. Execution of the serviceapplication 375 can cause the user device 370 to establish a connectionover the one or more networks 390 with a requester interface 325 of theon-demand transport management system 300.

In various aspects, the execution of the service application 375 cancause a user interface 372 to be generated on a display screen of theuser device 370. Using the user interface 372, the requesting user 374can generate and transmit a transport request 371 to the requesterinterface 325 of the transport system 300. In generating the transportrequest 371, the requesting user 374 can input a desired pick-uplocation, destination, and/or ride service. As provided herein,selectable ride services facilitated by the on-demand transport system300 include carpooling, standard ride-sharing, high-capacity vehicle(e.g., a van), luxury vehicle, a professional driver, AV transport,freight, package, or food delivery services, or certain combinations ofthe foregoing.

According to examples, the on-demand transport management system 300 caninclude a provider interface 315 that connects, via the one or morenetworks 390, with a fleet of transportation service provider vehicles380 available to provide on-demand transportation services to therequesting users 374. In various examples, the service provider vehicles380 can comprise a fleet of FAVs 389, any number of drivers driving HDVs387, and safety-driven autonomous vehicles (SDAVs) 381. In certainaspects, the human-driven vehicles 387 can also operate to providetransportation services at will, where the driver can execute a driverapplication 386 on a driver device 385 (e.g., a mobile computing device,smart phone, tablet computing device, etc.), causing the driver device385 to transmit provider location data 382 indicating the driver'slocation to the provider interface 315. The executing driver application386 can enable the driver of the HDV 387 to receive transportinvitations 338 indicating a pick-up location to rendezvous with amatched requesting user 374 to service a given transport request 371.

Likewise, any given SDAV 381 and FAV 389 in the fleet can transmit itscurrent SDAV location 383 and FAV location 388 respectively to theprovider interface 315 of the on-demand transport management system 300.As provided herein, the SDAV locations 383, FAV locations 388, anddriver locations 382 are collectively referred to as “provider locations384” of the service provider vehicles 380. The provider interface 315can transmit the provider locations 384 to a matching engine 320 of thetransport system 300. As further provided herein, the matching engine320 of FIG. 3 can correspond to the matching engine 255 shown in FIG. 2.

The matching engine 330 can receive the transport requests 371 from therequester interface 325, which can include respective pickup locationsor current locations of the requesting users 374. Based on the providerlocations 384, and using map data and/or traffic data, the matchingengine 330 can identify a set of candidate vehicles 323 to service thetransport request 371 (e.g., based on distance or time to a givenpick-up location). In doing so, the matching engine 320 can identifyvehicles proximate to the pickup location indicated in the transportrequest 371, and determine the set of candidate vehicles based on thevehicles being a predetermined distance or estimated time from thepickup location indicated in the transport request 371.

As provided herein, the matching engine 320 can further utilize a costoptimizer 345 in determining a most optimal vehicle to service a giventransport request 371. For example, once a transport request 371 isreceived, the matching engine 320 can initially utilize the currentlocation of the requesting user 374 to determine a candidate set ofvehicles 323 within a certain distance or time from the user's location.In some aspects, the candidate set of vehicles 323 can include a blendof HDVs 387, SDAVs 381, and/or FAVs 389 operating throughout theautonomy grid 105 and the given region in general. The cost optimizer345 can generate an estimated trip cost or revenue 348 for each vehiclein the candidate set 323 based on the trip route 324. The determinedcost or revenue 348 can further be based on a distance and/or estimatedtime for the overall trip between the pick-up location and the desireddestination, the ride service type (e.g., luxury vehicle, high capacityvehicle, carpool, etc.), usage cost (e.g., fuel or power use, on-boardservice features, network access, etc.). In various examples, thedetermined cost can further be based on a selected ride service type bythe user 374 (e.g., carpool), or can be optimized across multipleservices.

In further implementations, the matching engine 320 can select a mostoptimal vehicle based on trip classification 352 as determined by atrained trip classifier 350, which can filter out certain candidatevehicles 323 based on trip risk values 332 as determined by a trainedrisk regressor 330. Specifically, when a transport request 371 isreceived by the requester interface 325, a route optimizer 335 of theon-demand transport management system 300 can process the transportrequest 371 to determine one or more optimal trip routes 337. In someaspects, the on-demand transport management system 300 can run a set oftrained risk regressors 330 to determine respective aggregate trip riskvalues 332 for each of the trip routes 337. In doing so, each of therisk regressors 330 can determine a current set of conditions 399, whichcan include road conditions, weather conditions, lighting conditions,traffic conditions, and the like. Based on the nature of the trip route337 and the current conditions 399, the risk regressor 330 can determinethe trip risk value 332 for each trip route 337.

In certain implementations, each trip risk value 332 can be specific tothe utilization of a specific software version 346 for the trip route337. In this manner, a single risk regressor 330 and trip classifier 350combination may be specific to a single software version 346, where therisk regressor 330 determines the trip risk value 332 for a trip route337 by an AV 381, 389 using the software version 346, and the tripclassifier 350 ultimately determines whether an FAV 389, SDAV 381, or anHDV 387 can service the trip based on the trip risk value 332.Accordingly, for each received trip route 337 corresponding to atransport request 371, a trip risk value 332 can be determined for eachsoftware version 346 aggregated over the entire trip route 337. The tripclassifier 350 may then determine which vehicles can service the request371 based on the risk value 332. This determination, represented by thetrip classification 352, can indicate that the risk value 332 is toohigh to use any of the software versions 346, and therefore only HDVs387 can service the transport request 371. In other scenarios, such asin low traffic conditions late at night, the SDAVs 381 and FAVs 389 maybe advantageous over human drivers, who are typically more dangerous onthe road due to various factors, such as lack of visibility, drowsiness,impaired driving, etc. Accordingly, the trip classifier 350 or matchingengine 320 may also weigh human driving risk against the risk values 332from the risk regressor 330.

As described herein, the software version 346 may be verified orunverified. The trip classifier 350 can establish a set of riskthresholds for utilizing the software version by either an SDAV 381 oran FAV 389. For verified software versions 346, the set of riskthresholds can comprise use of the verified software version 346 by (i)an SDAV 381 as a first risk threshold, or (ii) both FAVs 389 and SDAVs381 as a second risk threshold. For new, or unverified software versions346, the set of risk thresholds can comprise use of the unverifiedsoftware version 346 by (i) SDAVs 381 for logging verification mileage,or (ii) SDAVs 381 but excluded for verification mileage (e.g., usedinstead to aid in training a new trip classifier 350). In any scenario,if the risk value 332 is above all risk thresholds, then only HDVs 387are available to service the transport request 371.

Conversely, if the trip risk value 332 is below all thresholds for averified software version 346, then the trip classifier 350 can enableall vehicles types (HDVs 387, SDAVs 381, and FAVs 389) to service thetransport request 371. Likewise, if the trip risk value 332 is below allthresholds for an unverified software version 346, then the tripclassifier 350 can enable HDVs 387 and SDAVs 381 to service thetransport request 371, as well can authorizing the use of the unverifiedsoftware version 346 for either logging verification mileage orexcluding the trip from a logged verification set. As further examples,if the software version 346 is unverified, the trip classifier 350 canenable use of SDAVs 381 executing the unverified software version 346 tolog mileage for verification based on the trip risk value 332 beingbelow a certain threshold. Accordingly, the trip classifier 350 canenable the SDAVs 381 having the unverified software version 346 toservice the transport request 371. Likewise, if the software version 346is verified, then the trip risk value 332 can enable the use of theverified software version 346 on SDAVs 381 and/or FAVs 389. In any case,the matching engine 320 will ultimately select an optimal vehicle toservice the transport request 371 across those vehicles authorized bythe trip classifier(s) 350, and other factors such as estimated tripcost or revenue 348, and estimated distance or time of the vehicle fromthe pick-up location.

The output from the trip classifiers 350 for any given trip route 337can comprise a trip classification 352 that identifies the vehicle typesauthorized to service the transport request 371 along the route 337, andthe specifics regarding execution of the software version 346 (e.g.,whether it is to be utilized for verification mileage). Thus, for asingle trip route 337, a risk regressor 330 can determine an aggregaterisk value 332 for the route 337 given the current conditions 399. Thetrip classifier 350 may then determine which vehicle types may servicethe trip route 337 based on the risk value 332, and whether the softwareversion 346 may be utilized in a verification set or for testing orsoftware training (e.g., a new risk regressor or trip classifier of thesoftware training system 301, such as the software management system 300of FIG. 2). For this single trip route 337, the matching engine 320 maythen select a most optimal vehicle from the candidate vehicles 323 basedon the trip classification 352 from the trip classifier 350.

Cumulatively, the trip classifications 352 from all trip classifiers 350can encompass every software version 346 as well as every potential triproute 337 for a given transport request 371. In certain implementations,in addition to weighing the expected cost or revenue 348 and estimatedtime, the matching engine 320 can also hierarchically decide whichvehicle to service the transport request 371 based on whether thesoftware version 346 is verified or unverified and/or whether anunverified software version 346 may be executed for verificationmileage. In one example, the matching engine 320 can prioritizeunverified software versions 346 that have been authorized by the tripclassifier 350 for verification mileage by the SDAVs 381. Thus, as anexample, given a candidate set of vehicles 323, the matching engine 320can favor SDAVs 381 in the candidate set 323 that include unverifiedsoftware versions 346 authorized by the trip classifiers 350 for loggingverification mileage. This enables more rapid software verification, andhence swifter implementation by FAVs 389.

Along these lines, the trip classification 352 can act as a filter ofthe candidate set of vehicles 323 for the matching engine 320.Accordingly, given a candidate set of vehicles 323 within a certainproximity of the pick-up location indicated in the transport request371, the trip classifications 352 can filter out vehicles and softwareversions 346 whose risk thresholds do not meet the trip risk value 332for the trip route 337. Of the remaining vehicles in the candidate set323, the matching engine 320 can base the ultimate selection on one ormore additional factors, such as estimated time of arrival to thepick-up location and/or trip cost or expected revenue 348. If the mostoptimal vehicle is an HDV 387, the matching engine 320 can generate atransport invitation 338 to the driver device 385 of the HDV 387, andthe driver can either accept or decline the invitation 338. If the mostoptimal vehicle is an SDAV 381 or an FAV 389, then the matching engine320 can transmit a set of transport instructions 332 to the SDAV 381 orFAV 389 indicating the software version 346 for execution and tripinformation (e.g., pick-up location and destination). In any case, thematching engine 320 may then provide a confirmation 334 to therequesting user 374 indicating identifying information for the matchedvehicle.

In various examples, the on-demand transport management system 300 canreceive data log data 391 from the SDAVs 381 and the FAVs 389, and storethe log data 391 in a set of AV state logs 348. The AV state logs 348can include—per vehicle—diagnostics and telemetry information, live orrecorded sensor data, and other data indicating a degradation level ofthe AV. In addition, the database 340 can store live driver data 347that indicates—per driver—the number of hours that the driver has beenon-duty and the driver's profile information, which can indicatepreferred driving areas, driver rating, an incident log (e.g.,indicating any collisions, accidents, or altercations of the driver),and/or the driving habits or characteristics of the driver. In furtherexamples, the database can also store fleet utilization data 398collected over time and indicating the most optimal use of the differentvehicle types and software for matching vehicles with the requestingusers 374. For example, the fleet utilization data 398 can indicateareas within the given region in which HDVs 387 are more optimallyutilized over SDAVs 381 or FAVs 389 (e.g., in terms of risk, revenuegenerated, or an optimization between risk and revenue). Conversely, thefleet utilization data 398 can indicate areas or locations within thegiven region where SDAVs 381 and/or FAVs 389 are most optimallyutilized. Based on the fleet utilization data 398, the on-demandtransport management system 300 can effectively move vehicle suppliesthrough trip classification and matching techniques described herein inorder to efficiently utilize the fleet of service provider vehicles 380at any given time. In one example, the on-demand transportationmanagement system 300 can do so by establishing a set of selectionpriorities based on the fleet utilization data 398 to move individualvehicles (e.g., through trip matching operations) to their most optimalareas and locations.

According to certain implementations, a specialized risk regressor 330can determine a generalized aggregate risk for a given trip route 337and then determine individual risk values for the trip route 337 foreach vehicle in a candidate set of vehicles 323. In suchimplementations, the risk regressor 330 can receive the candidate set ofvehicles 323 from the matching engine 320. For each vehicle, the riskregressor 330 can determine a risk score for servicing the transportrequest 371. For example, the risk regressor 330 can lookup AV statedata 343 and/or the live driver data 347 for each vehicle to output aset of candidate risk values 333 to the matching engine 320.

For a driver, the risk regressor 330 can determine the individual riskvalue 333 for the driver based on, for example, how long the driver hasbeen on-duty and the current and/or historical driving characteristicsof the driver (e.g., aggressive, fast, slow, gentle, normal). Indetermining the current or historical driving characteristics of thedriver, the on-demand transport management system 300 can receiveaccelerometer data or inertial measurement unit (IMU) data (e.g.,gyroscope data, magnetometer data, and accelerometer data) from thedriver's vehicle or the driver's computing device 385 (e.g., via accessto the device 385 through the driver app 386). The accelerometer or IMUdata can indicate hard braking, steering, and acceleration events thatthe risk regressor 330 can generalize into the driver's driving styleand weigh into the driver's individual risk score 333. In addition oralternatively, the on-demand transport management system can furtherreceive GPS data, image or video data, and/or audio data from amicrophone of the driver device 385 or vehicle hardware to determine theindividual risk value 333.

Accordingly, when the candidate set of vehicles 323 only includes HDVs387, the ultimate selection by the matching engine 320 can be heavilyweighted towards the individual risk value 333 of the drivers of thoseHDVs 387. This individualized risk assessment for drivers can enable theon-demand transport management system 300 to also provide notificationsto the drivers, either praising the driver for excellent, low-riskdriving, suggesting that the driver take a break, or cautioning thedriver to drive less aggressively. Such notifications can be provided tothe drivers via the driver app 386 executing on the driver's computingdevice 385.

For SDAVs 381, the risk regressor 330 can weigh the fact that the SDAV381 has a safety driver in case autonomous control fails. For both SDAVs381 and FAVs 389, the risk regressor 330 can determine a degradationlevel of the vehicle. The degradation level can include factors such asoutdated or older sensors and hardware, older software versions,calibration faults for the vehicle's sensors (e.g., misaligned LIDAR),faulty sensors (e.g., debris or grime on a camera lens), diagnosticfaults or failures, and the like. Based on the degradation level of thevehicle, and the generalized aggregate risk value 332 for the route, therisk regressor 330 can determine an individual risk value 333 for theSDAV 381 or FAV 389.

Accordingly, the matching engine 320 can make a final selection of avehicle based on each of the trip classifications 352, expected cost orrevenue 348, individual risk value 333, and the estimated time ofarrival to the pick-up location. Once the vehicle has been selected toservice the transport request 371, and the transport instructions 332 orthe transport invitations 338 have been accepted, the on-demandtransport management system 300 can hand over trip monitoring to anon-trip monitoring system 302, as described below with respect to FIG.4.

FIG. 4 is a block diagram illustrating an example on-trip monitoringsystem utilized in-connection with an on-demand transportationmanagement system. Once a driver or AV have been matched, the on-demandtransport system 401 (e.g., the on-demand transportation managementsystem 300 of FIG. 3) can notify the on-trip monitoring system 400 ofthe pairing. The on-trip monitoring system 400 can include networkinterface 415 that can connect with operating SDAVs 481 and FAVs 489through one or more networks 480. In addition, the network interface 415can also access any number of third party resources 490 over the one ormore networks 480 to receive third party data 492 that can indicate thecurrent conditions across an autonomy grid map 444 of the given region.For example, the on-trip monitoring system 400 can include a liveconditions monitor 420 that can access the third party data 492 todetermine current traffic data 422, live weather data 424, and/or eventdata 426 for the given region (e.g., parades, protests, bicycle orrunning races, gatherings, and the like).

The on-trip monitoring system 400 can further include a vehicle monitor460 that can receive AV log data 488 streamed or periodicallytransmitted from the SDAVs 481 and the FAVs 489. The AV log data 488 caninclude live telemetry and diagnostics data, live sensor data streams,and data indicating the AV's planned trajectory and overall route. Thevehicle monitor 460 can compile the AV log data 488 and the current setof conditions from the live conditions monitor 420 as a set of forwardroute parameters 464 for the SDAV 481 or FAV 489. The forward routeparameters 464 for each vehicle can be processed by a live riskregressor 425 that can dynamically determine an overall risk value 432across a remainder of the trip.

Example described herein recognize that conditions may change quiterapidly over the course of a single trip, such as traffic conditions,weather conditions, or lighting conditions. These changing conditionscan affect the autonomous performance of the SDAVs 481 and FAVs 489 suchthat current risks for the remainder of the trip may increase tounacceptable levels (e.g., when clouds begin to precipitate). In variousexamples, the live risk regressor 425 can quantify a forward trip riskvalue 432 for the SDAV 481 or FAV 489 at any given time. For example,the live conditions monitor 420 or the vehicle monitor 460 can identifychanges in weather conditions (e.g., via live image data from the AVs orlive weather updates). On a high level, these changes can trigger thelive risk regressor 425 to determine whether conditions are safe enoughfor the SDAVs 481 and FAVs 489 to operate in autonomous mode. On a lowerlevel, the SDAVs 481 and FAVs 489 can store multiple software versions,which can be rated for lower risk or higher risk autonomous operation(e.g., verified versions 451 versions versus unverified versions 452, orsoftware versions specifically created for certain conditions).

Based on the forward route parameters 464 for a given AV (SDAV 481 orFAV 489), the live risk regressor 425 can determine a forward trip riskvalue 432 for the AV. The live risk regressor 425 can output the forwardtrip risk value 432 to a live trip classifier 470, which can determinewhether the AV can continue using a current software version, continueusing a different software version, or must be decommissioned orserviced. The live trip classifier 470 can access a database 440 thatincludes the autonomy grid map 444, and software version logs 446 thatinclude the verified software versions 451 and the unverified softwareversions 452. Each of the software versions 451, 452 can be associatedwith one or more risk thresholds below which the software version may beused.

In certain examples, the forward trip risk value 432 for a given AV maybe higher than all risk thresholds of the live trip classifier 470. Insuch examples, the live risk regressor 425 can generate a decommissiontrigger 429 causing the vehicle monitor 460 to transmit a decommissioncommand 468 to the AV. The decommission command 468 can instruct the AVto pull over and park, find a nearest safe place to stop, or wait forthe risk to decrease. In such scenarios, if a passenger is beingtransported, the on-trip monitoring system 400 can transmit anotification to the on-demand transport system 401 to coordinate an HDV487, and SDAV 481, or a non-degraded FAV 489 to pick-up the passenger atthe stopped location of the AV.

In further examples, the degradation state of the AV (e.g., an SDAV 481or FAV 489) can further trigger a decommission command 468 from theon-trip monitoring system 400. For example, hard bumps can jostle theAV's sensor systems, cause disconnections in the AV's wiring, causemechanical faults (e.g., flat tires), misalignments, etc. The AV logdata 488 can indicate any misalignments or sensor faults and diagnosticsfailures that can contribute to the forward trip risk value 432 for theAV being unacceptably high. Accordingly, the on-trip monitoring system400 can transmit a decommission command 468 to the AV to compel the AVto, for example, drive to a nearest service station for recalibration orrepair, or hand over manual control to a human driver.

In variations, the live trip classifier 470 can determine that the riskvalue 432 is still within risk thresholds for autonomous operation, butwith a different software version than is currently executing on the AV.In such examples, the live trip classifier 470 can transmit a switchtrigger 472 to a software switching module 430. The switch trigger 472can identify which specified software version 451, 452 the AV is toexecute for the remainder of the trip. The software switching module 430can then transmit a software switch command 462 to the AV over thenetwork 480, instructing the AV to switch to the software versionspecified by the live trip classifier 470 for the remainder of the trip.

In certain aspects, the on-trip monitoring system 400 can also instructthe AVs to switch software versions at a pick-up location (e.g., executea verified software version 451 for the trip), at the drop-off location(e.g., execute an unverified software version 452 to log verificationmiles), or at specific triggering locations along the autonomy grid map444. Accordingly, a software switch command 462 can be triggered basedon the AV's location, the current conditions, or the AV's state (e.g.,on-trip with a passenger versus without a passenger).

According to some examples, the vehicle monitor 460 can also receivedriver state data 482 from the driver devices of the HDVs 487. Thedriver state data 482 can indicate whether the driver is on-trip (i.e.,transporting a passenger), awaiting a transport invitation, or off-duty.The driver state data 482 can also indicate a current location and routeof the HDV 487 that the driver is operating. As described with respectto FIG. 3, the on-demand transport management system 300, 401 canreceive and store driver data 347 that indicates the recent drivingcharacteristics of the driver, as well as how long the driver has beenon-duty. Because the on-demand transport system 300, 401 also monitorscurrent conditions 399, these driver data 347 can further be correlatedto the current conditions 399 to indicate the performance and drivingcharacteristics of the driver in all conditions, such as in rain, snow,at night or other times of the day, in fog, etc. According to examples,the vehicle monitor 460 can analyze the driver state data 482 togenerate a set of forward route parameters 464 for the driver of the HDV487. Utilizing the driver data 347 from the on-demand transport system401, the live risk regressor 425 can also calculate an individual,forward trip risk value 432 for the human driver of the HDV 487.

With this individual risk value 432 for the driver, the on-tripmonitoring system 400 can perform any number of functions, such asproviding notifications corresponding to the driver's risk value 432 tothe driver's computing device, or providing the on-demand transportsystem 401 with feedback for further matches (e.g., weighing thedriver's individual risk against the trip classifications for the SDAVs481 and FAVs 489). Furthermore, since the forward risk value 432 for thedriver can be route-specific, the live risk regressor 425 can identifyalternative routes for the driver that are less risky, and the on-tripmonitoring system 400 can transmit a transport update 494 to thedriver's computing device to reroute the driver over a less risky route.

In various examples, the on-trip monitoring system 400 can detect theend of a trip by an AV (e.g., either an SDAV 489 or FAVs 489), and candetermine an optimal post-trip option for the AV. For example, the routeoptimizer 335, 450 of the on-demand transport system 401 can access theautonomy grid map 444 to identify any number of routes 477 from a givendrop-off location of the AV. For each of the routes 477, the live riskregressor 425 can generate a trip risk value 432 given the currentconditions and/or the individual state of the AV as determined from theAV log data 488. Additionally or alternatively, the autonomy grid map444 may indicate various predetermined stopping or parking locations atwhich the AV can await another set of transport instructions 332. Instill further implementations, the on-demand transport system 401 canidentify areas or locations within the autonomy grid map 444 havinghigher or lower demand for the transportation services, and it may bedesired to move the AV to these areas of higher demand. Weighing each ofroute risk, local demand, and availability of a waiting area, theon-trip monitoring system 400 can determine a most optimal post-tripplan for the AV once a drop-off is made. Within a certain time prior to,during, or after drop-off, the on-trip monitoring system 400 cantransmit a set of post-trip instructions 496 detailing the post-tripplan for the AV. Further description of the post-trip instructions 496and decision-making is provided in the methodology discussion below.

It is contemplated that any of the functions between the logical blocksof FIGS. 2, 3, and 4 may be combined or excluded. For example, thefunctions of the AV software management system 200, on demand transportmanagement system 300, and the on-trip monitoring system 400 may evolveover time to specifically exclude HDVs within autonomy grids, or mayeventually exclude SDAVs (e.g., where unverified software versions areextensively scrutinized through simulation and/or verified on FAVs).Thus, the inclusion of logical blocks and description herein are notlimited to any single embodiment, and can therefore be substituted,included with other blocks, or excluded to result in any combinedembodiment of the functions described herein.

Autonomous Vehicle

FIG. 5 is a block diagram illustrating an example autonomous vehicle incommunication with on-demand transportation management systems, asdescribed herein. In an example of FIG. 5, a control system 520 canautonomously operate the AV 500 in a given geographic region for avariety of purposes, including transport services (e.g., transport ofhumans, delivery services, etc.). In examples described, the AV 500 canoperate autonomously without human control. For example, the AV 500 canautonomously steer, accelerate, shift, brake, and operate lightingcomponents. Some variations also recognize that the AV 500 can switchbetween an autonomous mode, in which the AV control system 520autonomously operates the AV 500, and a manual mode in which a safetydriver takes over manual control of the acceleration system 572,steering system 574, braking system 576, and lighting and auxiliarysystems 578 (e.g., directional signals and headlights).

According to some examples, the control system 520 can utilize specificsensor resources in order to autonomously operate the AV 500 in avariety of driving environments and conditions. For example, the controlsystem 520 can operate the AV 500 by autonomously operating thesteering, acceleration, and braking systems 572, 574, 576 of the AV 500to a specified destination. The control system 520 can perform vehiclecontrol actions (e.g., braking, steering, accelerating) and routeplanning using sensor information, as well as other inputs (e.g.,transmissions from remote or local human operators, networkcommunication from other vehicles, etc.).

In an example of FIG. 5, the control system 520 includes computationalresources (e.g., processing cores and/or field programmable gate arrays(FPGAs)) which operate to process sensor data 515 received from a sensorsystem 502 of the AV 500 that provides a sensor view of a road segmentupon which the AV 500 operates. The sensor data 515 can be used todetermine actions which are to be performed by the AV 500 in order forthe AV 500 to continue on a route to the destination, or in accordancewith a set of transport instructions 591 received from an on-demandtransport management system 590, such as the on-demand transportmanagement system 300 described with respect to FIG. 3. As providedherein, the transport management system 590 shown in FIG. 5 can furtherrepresent the AV software management system 200 of FIG. 2 and theon-trip monitoring system 400 of FIG. 4. In some variations, the controlsystem 520 can include other functionality, such as wirelesscommunication capabilities using a communication interface 535, to sendand/or receive wireless communications over one or more networks 585with one or more remote sources. In controlling the AV 500, the controlsystem 520 can generate commands 558 to control the various controlmechanisms 570 of the AV 500, including the vehicle's accelerationsystem 572, steering system 557, braking system 576, and auxiliarysystems 578 (e.g., lights and directional signals).

The AV 500 can be equipped with multiple types of sensors 502 which cancombine to provide a computerized perception, or sensor view, of thespace and the physical environment surrounding the AV 500. Likewise, thecontrol system 520 can operate within the AV 500 to receive sensor data515 from the sensor suite 502 and to control the various controlmechanisms 570 in order to autonomously operate the AV 500. For example,the control system 520 can analyze the sensor data 515 to generate lowlevel commands 558 executable by the acceleration system 572, steeringsystem 557, and braking system 576 of the AV 500. Execution of thecommands 558 by the control mechanisms 570 can result in throttleinputs, braking inputs, and steering inputs that collectively cause theAV 500 to operate along sequential road segments according to a routeplan 567.

In more detail, the sensor suite 502 operates to collectively obtain alive sensor view for the AV 500 (e.g., in a forward operationaldirection, or providing a 360 degree sensor view), and to further obtainsituational information proximate to the AV 500, including any potentialhazards or obstacles. By way of example, the sensors 502 can includemultiple sets of camera systems 501 (video cameras, stereoscopic camerasor depth perception cameras, long range monocular cameras), LIDARsystems 503, one or more radar systems 505, and various other sensorresources such as sonar, proximity sensors, infrared sensors, and thelike. According to examples provided herein, the sensors 502 can bearranged or grouped in a sensor system or array (e.g., in a sensor podmounted to the roof of the AV 500) comprising any number of LIDAR,radar, monocular camera, stereoscopic camera, sonar, infrared, or otheractive or passive sensor systems.

Each of the sensors 502 can communicate with the control system 520utilizing a corresponding sensor interface 510, 512, 514. Each of thesensor interfaces 510, 512, 514 can include, for example, hardwareand/or other logical components which are coupled or otherwise providedwith the respective sensor. For example, the sensors 502 can include avideo camera and/or stereoscopic camera system 501 which continuallygenerates image data of the physical environment of the AV 500. Thecamera system 501 can provide the image data for the control system 520via a camera system interface 510. Likewise, the LIDAR system 503 canprovide LIDAR data to the control system 520 via a LIDAR systeminterface 512. Furthermore, as provided herein, radar data from theradar system 505 of the AV 500 can be provided to the control system 520via a radar system interface 514. In some examples, the sensorinterfaces 510, 512, 514 can include dedicated processing resources,such as provided with field programmable gate arrays (FPGAs) which can,for example, receive and/or preprocess raw image data from the camerasensor.

In general, the sensor systems 502 collectively provide sensor data 515to a perception engine 540 of the control system 520. The perceptionengine 540 can access a database 530 comprising stored localization maps532 of the given region in which the AV 500 operates. The localizationmaps 532 can comprise a series of road segment sub-maps corresponding tothe autonomy grid map 105 described with respect to FIG. 1. As providedherein, the localization maps 532 can comprise highly detailed groundtruth data of each road segment of the autonomy grid map 105. Forexample, the localization maps 532 can comprise prerecorded data (e.g.,sensor data including image data, LIDAR data, and the like) byspecialized mapping vehicles or other AVs with recording sensors andequipment, and can be processed to pinpoint various objects of interest(e.g., traffic signals, road signs, and other static objects). As the AV500 travels along a given route, the perception engine 540 can access acurrent localization map 533 of a current road segment to compare thedetails of the current localization map 533 with the sensor data 515 inorder to detect and classify any objects of interest, such as movingvehicles, pedestrians, bicyclists, and the like.

In various examples, the perception engine 540 can dynamically comparethe live sensor data 515 from the AV's sensor systems 502 to the currentlocalization map 533 as the AV 500 travels through a corresponding roadsegment. The perception engine 540 can identify and classify any objectsof interest in the live sensor data 515 that can indicate a potentialhazard. In accordance with many examples, the perception engine 540 canprovide object of interest data 542 to a prediction engine 545 of thecontrol system 520, wherein the objects of interest in the object ofinterest data 542 indicates each classified object that can comprise apotential hazard (e.g., a pedestrian, bicyclist, unknown objects, othervehicles, etc.).

Based on the classification of the objects in the object of interestdata 542, the prediction engine 545 can predict a path of each object ofinterest and determine whether the AV control system 520 should respondor react accordingly. For example, the prediction engine 540 candynamically calculate a collision probability for each object ofinterest, and generate event alerts 551 if the collision probabilityexceeds a certain threshold. As described herein, such event alerts 551can be processed by the vehicle control module 555 and/or the routeplanning engine 560, along with a processed sensor view 548 indicatingthe classified objects within the live sensor view of the AV 500. Thevehicle control module 555 can then generate control commands 558executable by the various control mechanisms 570 of the AV 500, such asthe AV's acceleration, steering, and braking systems 572, 574, 576. Incertain examples, the route planning engine 560 can determine animmediate, low level trajectory and/or higher level plan for the AV 500based on the event alerts 551 and processed sensor view 548 (e.g., forthe next 100 meters or up to the next intersection).

On a higher level, the AV control system 520 can include a routeplanning engine 560 that provides the vehicle control module 555 with aroute plan 567 to a given destination, such as a pick-up location, adrop off location, or other destination within the given region. Invarious aspects, the route planning engine 560 can generate the routeplan 567 based on transport instructions 591 received from the on-demandtransport system 590 over one or more networks 585. According toexamples described herein, the AV 500 can include a location-basedresource, such as a GPS module 522, that provides location data 521(e.g., periodic location pings) to the on-demand transport system 590over the network(s) 585. Based on the AV's 500 location data 521, theon-demand transport system 590 may select the AV 500 to service aparticular transport request, as described above with respect to FIGS.2-4.

In various implementations, the database 530 can further store a numberof software versions 534 executable by the perception engine 540, theprediction engine 545, the route planning engine 560, and/or the vehiclecontrol module 555. Thus, at any given time, the AV control system 520can execute a current software version 537 that controls the manner inwhich the AV control system 520 autonomously operates the AV 500. Asdescribed herein, the software versions 534 can be verified orunverified, and can be executed by the control system 520 in response tosoftware switch commands 594 or the transport instructions 591 from thetransport management system 590.

In certain examples, the control system 520 can transmit or stream AVlog data 527 to the transport management system 590. The log data 527enables the transport management system 590 to provide updated transportinstructions 591 or software switching commands 594, and can furtherindicate a degradation level of the AV 500. As described herein, the logdata 527 can include a sensor data stream 515 from the AV's sensorsystems 502, and data corresponding to decisions, calculations, andcontrol inputs made by the AV control system 520, such as objectclassification by the perception engine 540, path prediction by theprediction engine 545, and control commands 558 generated by the vehiclecontrol module 555. Accordingly, the transport management system 590 canassess the AV control system's 520 performance against a nominalperformance range to determine if the AV 500 is operating nominally, orif a certain degradation exists in any one of the AV's autonomousfunctions.

In some aspects, the AV control system 520 can operate in accordancewith a set of safety standards, such as certainty probabilities withrespect to object detection, classification, and/or path prediction.Accordingly, when these certainty probabilities are not met, the AVcontrol system 520 can enter a stuck state, unable to progress further.Such stuck states may be caused by an indeterminate object, such as aplastic bag in front of the AV 500, or a significant occlusion in theAV's sensor view (e.g., a parked truck blocking a field of view of thesensor systems 502). According to certain implementations, when the setof safety standards are not met, the AV control system 520 canindependently switch to a different software version (e.g., a verifiedsoftware version instead of a test version). It is further contemplatedthat software version switch may be performed independently by the AVcontrol system 520 in response to making a passenger pick-up, a drop-offevent, or based on changing conditions (e.g., changing traffic, weather,road conditions, etc.).

Driver Device

FIG. 6 is a block diagram illustrating an example driver device utilizedby human drivers in-connection with an on-demand transportationmanagement system. The transport management system 690 shown in FIG. 6can represent the AV software management system 200 of FIG. 2, theon-demand transport management system 300 of FIG. 3, and/or the on-tripmonitoring system 400 of FIG. 4. In many implementations, the driverdevice 600 can comprise a mobile computing device, such as a smartphone,tablet computer, laptop computer, VR or AR headset device, and the like.As such, the driver device 600 can include typical telephony featuressuch as a microphone 645, a camera 650, and a communication interface610 to communicate with external entities using any type of wirelesscommunication protocol. In certain aspects, the driver device 600 canstore a designated application (e.g., a driver app 632) in a localmemory 630.

The driver device 600 can further include sensor features, such as aninertial measurement unit (IMU) 645. The IMU 645 can include anaccelerometer, gyroscopic sensor, and/or a magnetometer, and cangenerate sensor data 604 indicating the device's acceleration, velocityrelative to the Earth, and orientation. As provided herein, throughexecution of the driver app 632, the transport management system 690 canaccess the sensor data 604 from the IMU 645 and/or image data from thecamera 650. For example, the transport management system 690 can build adriver profile indicating the driving characteristics of the driverusing the sensor data 604. In variations, the transport managementsystem 690 can further utilize the sensor data 604 from driver device's600 throughout the given region to, for example, determine fractionalharmful events for specified road segments. As described in detailabove, the fractional harmful events may be context-dependent based on acurrent set of conditions.

In response to a user input 618, the driver app 632 can be executed byone or more processors 640, which can cause an app interface 642 to begenerated on a display screen 620 of the driver device 600. The appinterface 642 can enable the driver to initiate an “on-call” or“available” sub-state (of the normal application state), linking thedriver device 600 to the on-demand transport management system 690 thatfacilitates the on-demand transportation services. Execution of thedriver application 632 can also cause a location resource (e.g., GPSmodule 660) to transmit location data 662 to the transport system 690 toindicate the current location of the driver with the given region.

In many aspects, the driver can receive transport invitations 692 fromthe transport system 690, where the transport invitations 692 indicate aparticular pick-up location to service a pick-up request. The driver canprovide acceptance confirmations 622 back to the transport system 690indicating that the driver will service the pick-up request, or, in someaspects, decline the transport invitation 692 and await a subsequentopportunity. Upon submitting an acceptance confirmation 622, the driverapplication 632 can place the driver device 600 in an en route statewhile the driver drives to the pick-up location to rendezvous with therequesting user. Thereafter, the driver application 632 can initiate anon-trip sub-state (e.g., provide map directions to the requester'sdestination) while the driver transports the requesting user to thedestination.

Methodology

In the below discussions of the various methods of FIGS. 7-14, referencemay be made to reference characters representing certain logical blocks,engines, or modules described with respect to the systems diagrams ofFIGS. 2-6. Furthermore, certain blocks shown in FIGS. 2-6 may be recitedherein as computer systems that can perform the functions of one or moreof the logical blocks as shown and described with respect to FIGS. 2-6.Further still, certain methods, steps, or processes described withrespect to individual flow charts of FIGS. 7-14 may be combined withother steps or other flow charts, and need not be performed in therespective sequences shown.

FIG. 7 is a flow chart describing example methods of generalizingfractional harmful or risky events for path segments of a given region.In various examples, the below methods may be performed by an examplerisk regression system, corresponding to the risk regressors 230, 330,425, and the fractional harmful event quantifier 245 discussed withrespect to FIGS. 2-4. Furthermore, the risk regression system describedin connection with FIG. 7 may include functionality of the AV softwaremanagement system 200, the on-demand transport management system 300,and/or the on-trip monitoring system described herein. Referring to FIG.7, the risk regression system can collect log data and/or sensor datafrom vehicles operating along capability-in-scope paths of a givenregion (700). As described herein, the capability-in-scope paths cancomprise candidate paths for autonomous vehicles operation. The pathsneed not be solely paved road lanes, but can rather comprise any pathalong any combination of paved or unpaved roadways, aerial lanes, waterlanes, and the like. In various aspects, the risk regression system cancollect log data from AVs, operating within the capability-in-scopepaths (702). The log data can comprise sensor data from the AVs,telemetry data, diagnostics data, and recorded input data performed bythe AV's control system 520.

The risk regression system can further collect sensor data fromhuman-driven vehicles (non-autonomous vehicles) (704). For example, therisk regression system can receive IMU data or accelerometer data fromthe driver's computing device 600. In certain implementations, the riskregression system can time and/or location correlate the log data and/orsensor data with a current set of conditions (705). For example, thedata may be correlated to environmental conditions (706), pathconditions (707), path geometry and/or complexity (708), vehiclehardware (709), and/or traffic conditions (710). Such correlations allowfor the risk regression system to provide condition-dependent riskcalculations for any given path segment of a given road network (e.g.,an autonomy grid 105 on which AVs operate), which can be leveraged toassess current risk quantities for those path segments at any time andin any current set of conditions. In certain aspects, the riskregression system can further correlate the log data or sensor data to astatic set of risk parameters corresponding to nominal environmentalconditions and nominal path conditions (e.g., a dry road). In certainimplementations, the risk regression system can further collecthistorical harmful event data from any number of third party resources(e.g., traffic accident or collision report data).

According to various examples, the risk regression system can determinefractional risk values for each respective path segment of thecapability-in-scope paths (715). The risk regression system candetermine a fractional risk value for a given path segment specific to agiven set of environmental conditions (716). In doing so, for each givenpath segment, the risk regression system can determine fractional riskvalues for any number of environmental conditions, such as rainyconditions, degrees of rain (e.g., light, medium, heavy), roadconditions (e.g., wet, drying, dry, icy, snowy, etc.), sunny conditions,cloudy conditions, degrees of visibility (e.g., in smog or dust).Accordingly, when receiving a transport request having a pick-uplocation and destination, the risk regression system can determine thecurrent environmental conditions, and then aggregate the fractional riskvalues for each path segment of a given route for the trip to generate atotal risk value for servicing the trip along the route.

In various examples, the risk regression system can determine a defaultfractional risk value for a given path segment for nominal conditions(717). Nominal conditions can correspond to general dry surfaceconditions and typical daytime conditions (e.g., good lighting, sunshineor non-precipitation clouds, etc.). In further implementations, thefractional risk values determined by the risk regression system mayspecific to the software and hardware of the AV (718). For example, thesets of conditional fractional risk quantities can be specific to asingle software release that the AVs (SDAVs and FAVs) execute toautonomously operate throughout the autonomy grid 105. Additionally, thefractional risk quantities may also be specific to a hardwareconfiguration (e.g., a common set of sensors or sensor configuration).Accordingly, the risk regression system can compute aggregate riskvalues for AVs having common software executing on common hardware.Thus, in various implementations, for each new software release, a newrisk regression system may be trained, with new fractional risk valuescalculated for the path segments. It is contemplated that as timeprogresses and AV systems become increasingly more robust, thesefractional risk values will steadily decrease.

In certain implementations, the risk regression system may alsocalculate a set of generalized fractional risk values for each pathsegment based on, for example, lane geometry, complexity (e.g., trafficsignals and signs, intersecting lanes, bike lanes, crosswalks, blindturns, historical harmful events, etc.) (719). Accordingly, the riskregression system can also function to provide generalized aggregaterisk quantities for any particular route given a current set ofconditions. Such generalized aggregate risk quantities can be utilizedto route AVs and HDVs along lower or lowest risk routes accordingly. Instill further examples, the risk regression system can further determinethe fractional risk quantity for each path segment based on off-vehiclereplay of AV-logged data through new software, test track evaluation ofthe current system-under-test, actuarial statistics, and drivingresearch publications.

In various examples, the risk regression system can receive transportroute data for an on-demand transport request (720). Executingconcurrently with the on-demand transport management system 300, therisk regression system can further receive on-demand transport requests,and, for each transport request, the risk regression system candetermine one or more optimal routes between a pick-up location anddestination of the transport request—denoted as reference “A” in FIG. 7.These one or more optimal routes can correspond to the transport routedata received by the risk regression system. Thus, for each transportrequest and each route, the risk regression system can determine currentconditions across the a set of possible routes for the transport request(725). In further examples, the risk regression system can predict a setof condition over the course of the trip (e.g., in general or along eachroute) (725). In various examples, the current or predicted conditionscan include environmental conditions, weather conditions, whether theroute involves road construction, road surface conditions, trafficconditions, any predicted or scheduled events, time of day, day of theweek, and the like. The risk regression system may then execute a riskregression method using the fractional harmful event data—orconditions-based fractional risk values described herein—to determine anaggregate risk value for the route (730).

In some aspects, the risk regression system can transmit the aggregaterisk value to the on-demand transport system to facilitation vehicleand/or route selection for the trip (735). In other aspects, the riskregression system can determine a most optimal route for based on theaggregate risk values, and determine whether to enable SDAVs and/or FAVsto service the transport request. For example, the risk regressionsystem can execute concurrently with a trip classifier that enablesSDAVs and FAVs to service any given transport request based on trip riskin accordance with a set of risk thresholds described herein. Once anSDAV, FAV, or HDV is selected to service the transport request, the riskregression system can actively monitor the trip to dynamically determineaggregate risk of a remainder of the trip, as described in detail below(740). In doing so, the risk regression system can monitor for changingenvironmental conditions (742) and changing traffic conditions (744)that may affect the fractional risk values.

FIG. 8 is a flow chart describing example methods of matching atransport request with a service provider vehicle using risk regressionand trip classification. The below processes described with respect toFIG. 8 can be performed by example trip classifiers 250, 350, 470executing concurrently with an on-demand transport management system300, the AV software management system 200, and/or the on-tripmonitoring system 400 of FIGS. 2-4. Accordingly, the below discussion ofan on-demand transport management system can include functionality fromone or more of the foregoing. Referring to FIG. 8, the on-demandtransport management system can manage an on-demand transport servicelinking requesting users with available vehicles (800). These vehiclescan include FAVs (802), SDAVs (803), and HDVs (804), operating withinthe autonomy grid 105 and throughout the given region.

According to various examples, the transport management system canreceive transport requests from requesting users (805). The transportrequests can include a pick-up location (807) and a destination (809).In certain implementations, the transport management system candetermine one or more optimal routes for the transport request (810).For example, the transport management system can identify a set ofroutes, and select a route that has the lowest estimated time todestination based on such factors as current traffic conditions,projected traffic conditions, and distance. In variations, the transportmanagement system can first determine the aggregate risk values for eachroute prior to selecting a most optimal route for the trip basedpartially on risk.

The transport management system can determine a risk quantity for eachof the one or more optimal routes (815). According to various examples,the transport management system can determine the aggregate riskquantity through coordination with the risk regression system describedwith respect to FIG. 7, and represented by reference “A” in FIG. 8.Thus, the risk quantity determined at step (815) can be based onhistorical fractional harmful event data, a current set of conditions,and the aggregated fractional risk values as determined by the riskregression system. The transport management system may classify the tripbased on the aggregated risk quantity and a set of risk thresholds(820). In classifying the trip, the transport management systemultimately determines which vehicle types (822) executing which softwareversion (if any) are certified to service the transport request (824).Detailed description of the software version precertification andverification is provided below with respect to FIG. 9, and isrepresented by reference “B” in both FIGS. 8 and 9. In particular, eachsoftware version and vehicle type may be associated with a riskthreshold. In further implementations, the use of an unverified softwareversion can be attributed to two distinct risk thresholds—a first riskthreshold for including the trip in its verification mileage set, and asecond risk threshold for excluding the trip from its verification set.

As described herein, the transport management system may run a pluralityof on-trip classifiers (e.g., on a backend datacenter), eachrepresenting a software version stored on the SDAVs or FAVs operatingthroughout the autonomy grid 105. Accordingly, the transport managementsystem can receive a set of trip classifications from the multiple tripclassifiers, with each classification identifying the software versionand the authorized vehicles (e.g., SDAV, FAV, and/or HDV) for servicingthe transport request based on the calculated aggregate risk value ofthe route. The trip classifiers can each establish safety or riskthresholds for each software version. In certain variations, the tripclassifiers can also establish separate risk thresholds for whether theexecution of a software version for a trip is to be used forverification mileage or for other purposes (e.g., training a new tripclassifier). In further examples, the trip classifiers can establishseparate risk thresholds for SDAVs versus FAVs executing the samesoftware version (e.g., due to the fallback of having a safety driver).Accordingly, the overall trip classification answers which vehiclesexecuting which software versions are authorized to service a trip overa specified route given the current set of conditions.

According to many examples, the transport management system candetermine a candidate set of vehicles to service the transport request(825). In certain aspects, the candidate set of vehicles can bedetermined based solely on distance or time to the pick-up location(826). For example, the transport management system can establish ageofence encompassing a certain proximity around the pick-up location,and include any vehicle within the geofence in the candidate set ofvehicles. In further aspects, the candidate set of vehicles can also bedetermined based on the aggregate risk value for the trip (828). Forexample, the transport management system can determine the overall riskfor the trip and the trip classification prior to determining the set ofcandidate vehicles.

The transport management system may then determine whether to enableselection of the various vehicle types (e.g., between HDVs, SDAVs,and/or FAVs), or select a most optimal vehicle from the candidate set toservice the transport request (830). In selecting the most optimalvehicle, the transport management system can filter out any vehicle andsoftware combination whose established risk thresholds do not meet theaggregate risk value for the trip, as determined by the tripclassification(s). In general, the trip classification enables thetransport management system to select one of a human driver (832), anSDAV (833), or an FAV (834) to service the transport request. Thetransport management system can make the final selection based on anoptimization between risk, distance or time to the pick-up location,and/or expected revenue generated by each vehicle. If the selectioncomprises a human driver, then the transport management system cantransmit a transport invitation to the driver (835), which the drivercan accept or decline. If the driver accepts, then the transportmanagement system can receive a confirmation from the driver (845).

However, if the selected vehicle is either an SDAV or an FAV, then thetransport management system can transmit a set of transportationinstructions to the AV, instructing the AV to rendezvous with therequesting user at the pick-up location and to transport the requestinguser to the requested destination (840). In further implementations, thetransport instructions can further include the software version that theAV is to execute while servicing the transport request. Thereafter, thetransport management system can monitor the AV's progress to therendezvous point (i.e., the pick-up location) and onwards to thedestination (850). Detailed discussion of the on-trip monitoring isprovided below with respect to FIGS. 10A and 10B, and is represented bythe references “C_(1,2)” in FIG. 8, “C₁” in FIG. 10A, and “C₂” in FIG.10B—which describe the on-trip monitoring steps extending from step(850) and discussed throughout the present disclosure.

FIG. 9 is a flow chart describing example methods of simulation-basedprecertification and verification of AV software, according to variousexamples. The below steps discussed with respect to FIG. 9 may beperformed by an example AV software management system described hereinwith respect to FIG. 2. Furthermore, in some aspects, the stepsdiscussed in FIG. 9 may flow from reference “B” extending from block(824) in FIG. 8. Referring the FIG. 9, the AV software management systemcan receive a new AV software version or software update that relates toAV operation (900). A new AV software version or software update can becreated for virtually any purpose, such as the expansion of the autonomygrid map 105 (901), updating AV capabilities, such as improvements tosignaling intent or performing off-map functions (902), updating AVhardware, such as including new sensors or excluding redundant sensors(903), and updating AV operations, such as updates to the AV's detectionor stopping distances, response behavior, object classification or pathprediction updates, and the like (904).

In various examples, the AV software management system can utilizeprevious verified software versions to generate one or more simulationsfor the new software version (905). In one aspect, the simulations canbe generated by human software engineers using recorded AV logs andprevious software versions. In certain variations, the AV softwaremanagement system can run the new software version through a set ofdefault simulations for an initial verification. According to examples,the AV software management system can execute a full forward simulationon new software using real-world log data from AVs operating throughoutthe given region (910). Additionally or alternatively, the AV softwaremanagement system can execute Monte Carlo simulations for certain edgecases (920). These edge cases can correspond to higher fractionalharmful event scenarios (922), variable conditions (923), or hardware ordiagnostics failures (924).

The AV software management system can further adjust simulationparameters to further refine the simulation, and further execute thesimulations (925). For example, the AV software management system caninclude additional actors, such as other vehicles or AVs, pedestrians,and other objects (927). The AV software management system can furthersimulate various types of faults or failures (929). In each step of theforegoing simulation process, the AV software management system cangenerate a set of simulation results that enable engineers to makerefinements to the AV software or verify the software for furthersimulation or for real-world use. In doing so, the AV softwaremanagement system can verify the new software's outputs, trajectoryplans, decision-making, and/or responses (930). For example, the AVsoftware management system can verify such actions against a previouslyverified software version (932). In other examples, the AV softwaremanagement system can verify the actions against generalized humanperception and decision-making (934), such as a comparative simulationof an average human driver. Detailed discussion of this generalizationof human perception and decision-making in the context of driving—anddetermining risk associated with such human factors—is provided belowwith respect to FIG. 11, and is represented by reference “D” extendingfrom sub-block (934).

As provided herein, after running through a series of simulation tests,the AV software management system can determine whether the softwareversion is pre-certified (935). In other words, the AV softwaremanagement system can determine whether the software version is readyfor use on SDAVs (or FAVs) for real-world testing or execution. Thisdecision can be based on a set of performance metrics established by theAV software management system, such as a sequence of nominal ranges thatthe AV software version must be within in order to be pre-certified. Forexample, the simulations can identify whether the software versionperforms as well as or better than previously verified software versionsfor its stated purposes. If the software release does not perform inaccordance with the software management system's nominal ranges (937),then the AV software management system can generate a targetedsimulation report to enable debugging of the software version (940). TheAV software version may then be run through the precertificationsimulations again in steps (905) through (930).

If the AV software versions passes precertification (939), then on ahigh level, the AV software management system can distribute the newsoftware version to AVs (e.g., only SDAVs) operating throughout thegiven region for real-world safety verification (960). In variousexamples, the AV software management system can train a new riskregressor to couple with the new software version (945). The new riskregressor can determine fractional risk values for a hypothetical AVexecuting the new software version along each path segment of the givenregion. The AV software management system can further train a new tripclassifier to couple with the new software version (950). As describedherein, the new trip classifier can establish risk thresholds for thenew software version in servicing transport requests (952). In furtheraspects, the new trip classifier can also establish verificationparameters (954), such as verification miles needed before the softwareversion can be distributed to FAVs.

As provided herein, the trained risk regressor and trip classifier forthe new software version may be trained at any stage prior todistribution of the software release to the SDAVs. Furthermore, it iscontemplated that certain limited software releases may be fullycertified through simulation. Other more comprehensive software releasesmay require extensive real-world verification prior to execution onfully autonomous vehicles. According to various examples, the AVsoftware management system may pre-certify the new software release forreal-world SDAV testing (955). By limiting the release to SDAVs, the AVsoftware management system can leverage the added flexibility of havingtrained safety drivers as a mitigation against any unforeseeable issues.(e.g., stuck states). The AV software management system may thendistribute the new software versions to the SDAVs operating throughoutthe given region for safety verification (960). Detailed description ofthe verification process for the software release is provided below withrespect to FIG. 12, and is represented by reference “E” in both FIGS. 9and 12.

FIG. 10A is a flow chart describing example methods of dynamic softwareversion and/or autonomy mode switching, according to variousimplementations. The below processes discussed with respect to FIG. 10Amay be performed by an example on-trip monitoring system in connectionwith an on-demand transport management system described with respect toFIGS. 3 and 4. Furthermore, the steps shown in FIG. 10A can flow fromblock (850) of FIG. 8, represented by reference “C₁” in both FIGS. 8 and10A. Referring to FIG. 10A, the on-trip monitoring system can receiveand monitoring AV data indicating the current state of AVs operatingthroughout the given region (1000). The AV data can include AV telemetrydata indicating the AV's location, velocity, direction of travel, routeplan, and/or trajectory plan (1002). In further implementations, the AVdata can include diagnostics data indicating the performance of AVhardware and/or mechanical system (1004). These systems can include theAV's sensor systems, computer systems, engine, cooling, tires, brakes,suspension, communications systems, electronic control unit, and thelike.

The on-trip monitoring system can further monitor the dynamicenvironmental conditions for the given region in general, and/or localto the AV while the AV is on-trip (1005). In various aspects, theon-trip monitoring system can further dynamically or periodicallydetermine an aggregate risk value for the remainder of the trip (1010).As an example, with equal environmental conditions and AV conditions,the risk value for the trip remainder would steadily decrease due to theaggregation of less fractional risk values of path segments for thetotal trip. However, examples described here recognize that conditionsare constantly changing (e.g., traffic conditions, weather conditions,vehicle conditions, etc.), and can be individually factored into liverisk calculations for the AV. Thus, the aggregate risk for the tripremainder may be based on such conditions (1012), based on the state ofthe AV (1013), and based on the remaining route of the AV (1014).

In certain examples, the determination of the remaining risk for the AVcan be based on the original thresholds of the trip classifier.Accordingly, the on-trip monitoring system can determine whether theremaining risk exceeds the nominal thresholds established by the tripclassifier (1015). If not (1017), then the on-trip monitoring system cancontinue monitoring the trip and dynamically calculate the aggregaterisk for the trip remainder (1010). However, if the remaining risk doesexceed the nominal risk thresholds (1019), then the on-trip monitoringsystem can determine whether a verified software version having higherrisk thresholds is available on the AV (1020). In some aspects, theon-trip monitoring system can also determine whether an unverifiedsoftware version is available on the AV, and that has higher riskthresholds. For example, the AV may be executing an unverified testsoftware version initially having relatively low risk thresholds toensure maximum safety while logging verification miles. While on-trip,changing conditions (e.g., increased traffic) can cause the aggregaterisk to exceed these thresholds, requiring the AV to pull over and stop.If a more optimal software version is not available (1022), then theon-trip monitoring system can transmit a manual mode command to the AV,causing the AV to hand over manual control to a human safety driver(1025). However, if a more optimal software version is available (1024),then the on-trip monitoring system can select a software version havingthresholds within the current aggregate risk for the trip remainder(1030). In one aspect, the on-trip monitoring system can perform alookup in a stored AV profile to determine which software versions theAV has stored thereon, and select from this stored set of softwareversions. The on-trip monitoring system may then transmit a switchcommand to the AV to cause the AV to switch to the selected softwareversion (1035).

In monitoring the AV's progress, the on-trip monitoring system may alsodetermine whether the current aggregate risk for the remainder of thetrip is within the risk thresholds of a preferred software version(1040). If so (1042), then the on-trip monitoring system can transmit aswitch command to the AV to switch to the preferred software version(1045). For example, an important software update may require extensivereal-world verification, and can therefore be prioritized forverification mileage. If the aggregate risk for the trip falls below arisk threshold for the preferred software version, then the on-tripmonitoring system can facilitate increased verification mileage for thepreferred software version by enabling dynamic switching as describedherein.

Scenarios for FAVs, in which handing over control to a safety driver isnot an option, are also contemplated. At decision block (1040), if theaggregate risk for the trip remainder is not within any of the riskthresholds of the software versions stored on the FAV (1044), then theon-trip monitoring system can determine whether the FAV is in a degradedstate (1050). For example, the on-trip monitoring system can analyzediagnostics data, calibration data, or log data in general of the FAV(1052). In further examples, the on-trip monitoring system can perform alookup in a log database for AV to determine a time since the AV waslast serviced (1054). For example, the AV log data can indicate when thesensor systems of the AV were last calibrated, or the quality of sensordata from the AV's sensor systems, the age of the AV's sensor andcomputational systems, and the like.

If the AV is in a degraded state, then the on-trip monitoring system candecommission the AV (1055). For example, the on-trip monitoring systemcan transmit a command to the AV to travel to a service station orcentral facility to receive hardware and/or software upgrades (1056). Invariations, the on-trip monitoring system can transmit a command to theAV for servicing (1057). The servicing can entail hardware servicing,such as LIDAR calibration or alignment, lens cleaning for the AV'scamera systems, or general mechanical servicing (e.g., changing brakes,fluids, tires, oil, etc.). In further variations, the on-trip monitoringsystem may simply transmit a command to the AV to park until conditionsimprove (1058). For example, the on-trip monitoring system can determinethat current precipitation or traffic conditions will dissipate shortly,causing the remaining risk to decrease and enabling the AV to continueon its current route plan.

FIG. 10B is a flow chart describing example methods of post-trip AVmanagement. Post-trip management examples described herein may beperformed by an on-trip monitoring system as described with respect toFIG. 4, or a combination of an on-demand transport management system andan on-trip monitoring system as described with respect to FIGS. 3 and 4.Furthermore, the processes described in FIG. 10B can flow from block(850) of FIG. 8, represented by reference “C₂” in both FIGS. 8 and 10B.As described herein, the on-trip monitoring system can monitor tripsperformed by AVs throughout a given region. As further described, eachtrip can correspond to a passenger pick-up, transportation to a drop-offlocation, and a passenger drop-off at the drop-off location. Referringto FIG. 10B, the on-trip monitoring system can determine a set ofpost-trip options for each on-trip AV (1060). In various examples, theset of post-trip options can include approved stopping locations (1062)and/or a number of destination egress routes (1064). As provided herein,the destination can comprise the drop-of location of the passenger, andthe post-trip options can comprise any decision to be made for or by theAV after dropping off the passenger. Furthermore, the on-trip monitoringsystem can perform the operations described with respect to FIG. 10Bprior to drop-off (e.g., when the AV crosses a certain thresholddistance or estimated time of arrival to the drop-off location), or atthe time of drop-off. Ultimately, the on-trip monitoring system selectsthe most optimal post-trip option for the AV.

In determining the egress routes from the drop-off location, the on-tripmonitoring system can leverage the risk regression tools describedherein to determine risk values for path segments leading away from thedrop-off location (1065). In some aspects, the risk values can compriseaggregates of fractional risk values for equal path distances leadingaway from the drop-off location. As described herein, the risk valuescan be based on current conditions, such as road, traffic, and weatherconditions (1067). In some aspects, the on-trip monitoring system canfurther look up each software version stored on the AV—verified andunverified—and determine a risk value for each egress route and softwareversion combination (1069). The resultant set of risk values can beutilized by the on-trip monitoring system in determining a most optimalpost-trip option.

In certain implementations, the on-trip monitoring system can determineor predict transportation demand at the destination proximity (1070).For example, prior to the arrival of the AV at the drop-off location theon-trip monitoring system can coordinate with the on-demand transportmanagement system to determine transportation demand from requestingusers within an area surrounding the drop-off location (e.g., within amile of the drop-off location). The on-trip monitoring system may thendetermine whether the demand exceeds a predetermined demand threshold(1075). In some aspects, the demand threshold can be determined incomparison to surrounding areas of the autonomy grid 105, or the givenregion in general. For example, to bolster efficiency of the on-demandtransportation service, the on-trip monitoring system can coordinatewith the SDAVs and FAVs to move transportation supply to anticipated orcurrent areas of relatively higher demand within the autonomy grid 105.

If the area within a certain proximity of the drop-off location doesexceed the demand threshold (1077), then the on-trip monitoring systemcan wait for a transport request from within the proximity to includethe AV in the candidate set of vehicles to service the transport request(1080). Accordingly, the on-trip monitoring system can transmit a parkcommand or circle around command to the AV until a match is made betweenthe AV and a nearby requesting user (1085). For example, at the time ofdrop-off, the AV or the on-trip monitoring system can scan the localenvironment for an available and safe place for the AV to stop (e.g., aparking space or predetermined waiting area). If an available placeexists or appears, the on-trip monitoring system can instruct the AV topark and wait. However, if no available place appears (e.g., if the AVis in a high traffic urban environment), then the on-trip monitoringsystem can instruct the AV to continue driving until another match ismade for the AV, or until a parking location materializes.

If the demand threshold surrounding the drop-off location is notexceeded (1079), in general, the on-trip monitoring system can determinea most optimal post-trip plan for the AV (1090). In doing so, theon-trip monitoring system can analyze sensor data from the AV or otherAVs near the drop-off location, receive reports from other AVs ordrivers indicating available waiting areas (e.g., relatively emptyparking areas), analyze historical data from drivers and AVscorresponding to waiting areas. Accordingly, the on-trip monitoringsystem can identify a most optimal stopping location for the AV (1091),or a lowest or relatively low risk egress route (1092). In certainscenarios, the on-trip monitoring system can update the AV's operation(1094). For example, the on-trip monitoring system can instruct the AVto execute an unverified software version to log verification mileage,to recharge or refuel, drive to a home location, and the like. Asdescribed herein, the on-trip monitoring system can also determine areaswithin the autonomy grid 105 having high relative demand, and can alsoinstruct the AV to drive to an area of high transportation demand(1093). Accordingly, the on-trip monitoring system, once a most optimalpost-trip option is determined, the on-trip monitoring system cantransmit the post-trip command(s) to the AV to cause the AV to executethe most optimal post-trip plan (1095).

FIG. 11 is a flow chart describing example methods of evaluating AVsoftware releases against human and/or AV driving data, according toexample described herein. The below processes described with respect toFIG. 11 may be performed by an example AV software management systemdescribed with respect to FIG. 2. Furthermore, in certain examples, thesteps discussed below in connection with FIG. 11 may flow from block(934) of FIG. 9, and represented by reference “D” in both FIG. 9 andFIG. 11. Referring to FIG. 11, in various examples, the AV softwaremanagement system can collect log data from an AV fleet operating alongrespective routes within an autonomy grid 105 of a given geographicregion (1100). Based on the log data, the software management system candetermine fractional harmful event values, or fractional risk values,for each path segment (1105). In some aspects, the software managementsystem can do so for each lane segment of a set of capability-in-scopelanes throughout an entire geographic region. In other aspects, thesoftware management system can determine fractional risk values forpaths segments included within a mapped autonomy grid 105. Furthermore,each fractional risk value can be variable condition-dependent (e.g.,based on any set of weather, road, lighting, vehicle traffic, pedestriantraffic, and/or other environmental conditions) (1107). Still further,each path segment can also be associated with a nominal risk valuecorresponding to nominal conditions (e.g., normal, dry road and weatherconditions) (1109).

In various implementations, the AV software management system cancollect historical data of harmful events for the given region (1110).For example, the harmful events can correspond to traffic accidents,collisions between vehicles, pedestrians, bicyclists, etc. The AVsoftware management system can classify the harmful events according totype, such as vehicle collisions, collisions between vehicles andpedestrians, collisions between vehicles and bicyclists, single vehicleevents (e.g., a car crashing into a light post, telephone pole, orbuilding), impaired driving events (e.g., a drunk driver beinginvolved), incidents involving motorcyclists, the road conditions,weather conditions, and traffic conditions during the event, school zoneevents, etc. The AV software management system can also classify theharmful events on a sliding scale in terms of significance orconsequence, such as multiple fatality events, single fatality events,serious injury events, minor injury events, or no-injury events.

In some aspects, the AV software management system can further classifythe harmful event based on respective demographics of the deceased orthe victims of harmful events (e.g., age and chosen gender),demographics of the at-fault party or parties, and the like. Suchharmful event data can be collected from third party resources, such asincident reports (e.g., police reports), news sources, or direct reportsfrom drivers (1112). The harmful event data may also be collected fromsensor resources from vehicles, such as AVs or driver devices (1113). Incertain examples, the actual control input data from vehicles (e.g.,indicating steering, braking, and acceleration inputs and reaction timesfor humans) can be collected and directly or indirectly compared with AVsoftware responses. In collecting and parsing the harmful event data,the AV software management system can perform clustering operations todetermine common behavior corresponding to locality (e.g., a blindcorner or dangerous intersection), or corresponding to driver type(e.g., aggressive, gender-specific, age-specific, etc.) (1114).Accordingly, the AV software management system can cluster drivers intogroups based on risk, and can further cluster types of locations wherethe harmful events are typically occurring (e.g., certain types ofintersections, highway segments, merge locations, pedestrian-denseareas, complex or confusing areas, roads having little or no shoulder,and the like).

The AV software management system may then determine fractional harmfulevent values for each path segment of the given region for human-drivenvehicles (1115). As described herein, these fractional harmful eventvalues per path segment can also be condition-dependent. Thesefractional harmful event values can be leveraged for any number ofbeneficial utilizations, such as increasing road safety for all vehiclesin general, or supporting planning commissions in designing orconfiguring road segments and intersections. Flowing from block (1115)are two such processes represented by reference “F” and reference “G,”which are described below with respect to FIGS. 13 and 14.

Referring back to FIG. 11, the AV software management system mayoptimize risk between the fractional harmful events for human-drivenvehicles (HDVs) and the fractional harmful events for AVs (e.g., ingeneral or per software release for SDAVs and FAVs) across routesthroughout the given region (1120). In doing so, the AV softwaremanagement system can ultimately determine which paths or routes arebetter utilized—or more safely utilized—by AVs versus humans and viceversa. In one basic example, the AV software management system canidentify the riskiest aggregate paths or path segments for HDVs (1125),and the safest aggregate paths or path segments for AVs (1130). Based onthese paths or path segments, the AV software management system candetermine a set of paths for the HDVs to avoid, and a set of paths forthe AVs to avoid. In further examples, the AV software management systemcan optimize overall path classifications based on the fractional riskvalues determined for both AVs and HDVs. Such path classifications canalso be dynamic in nature (e.g., based on a current set of conditions).In classifying the paths or routes throughout the given region, the AVsoftware management system can determine which paths are more optimalfor AVs and which paths are more optimal for HDVs in terms of safety orrisk.

Based on the optimizations for paths or routes, the AV softwaremanagement system can establish capability-in-scope paths for AVoperation throughout the given region (1135). In further examples, thedata generated by the AV software management system can also be utilizedto identify certain roads or lanes in which full replacement of HDVs byAVs may be overwhelmingly desirable in terms of safety or alleviation oftraffic. According to various examples, the AV software managementsystem can then determine routes for AV operation based on the riskoptimization(s) (1140). In doing so, the software management system canestablish conditional risk thresholds for each path (1142). Accordingly,the AV software management system can establish and/or expand a baselineautonomy grid 105 for training risk regressors and trip classifiers, andto facilitate software simulation and development for AV operation(1144). The processes described with respect to FIG. 11 also allow forintrinsic evaluation of any AV software release against human driving.

FIG. 12 is a flow chart describing example methods of software releaseverification for execution by fully autonomous self-driving vehicles,according to examples described herein. The below steps of FIG. 12 maybe performed by an example AV software management system described withrespect to FIG. 2. Furthermore, in certain examples, the steps discussedin connection with FIG. 12 may flow from block (960) of FIG. 9, oraccompany the processes discussed with respect to FIG. 9. Referring toFIG. 12, the AV software management system can establish a set ofverification threshold for a new software release based on simulationdata (1200). In doing so, the AV software management system canestablish a threshold mileage per harmful event (MPHE) threshold incomparison to historical harmful event data from HDVs and/or AVs (1202).In certain implementations, the AV software management system can alsoestablish a threshold confidence level that must be achieved before agiven software release is verified for full autonomous use (1204).

In various implementations, the AV software management system can setrisk thresholds for a new software release for servicing requested rides(e.g., corresponding to the functions of the trip classifier examplesdescribed herein) (1205). These risk thresholds can correspond to anaggregated risk value for a trip route as calculated by a risk regressor(1207). For example, if the aggregate risk value is higher than the riskthreshold for the new software version, then the trip classifier canreject the software version for execution on the trip. The riskthresholds can also be established for a variety of trip conditions,such as weather, road, and/or traffic conditions (1208). For example,the risk for executing the software version may increase or decrease invariable weather conditions or denser traffic conditions. Likewise, thesoftware release may be tailored to deal with certain conditions orenvironments, such as precipitation, and thus the risk thresholds forthe software release may also vary based on the trip conditions. Infurther aspects, the AV software management system can also establishcertain risk thresholds based on the changing nature of the AV's state(1209). In other words, the AV software management system canindividualize risk thresholds for individual AVs based on a degradationlevel of the AV, as described herein. As further described herein, thenew AV software version can also be distributed specifically to SDAVs inorder to leverage the added protection of a safety driver for verifyingthe software version.

In certain aspects, the AV software management system may then collectlog data from the SDAVs utilizing the new software release (1210). Infurther aspects, the AV software management system can also evaluateSDAV autonomy performance in executing the new software release againsthuman fractional harmful event data for each route the SDAV traverses(1215). Throughout the log data collection and evaluation, the AVsoftware management system can determine whether the verificationthresholds for the new software release have been met (1220). If not(1222), then the software management system can either continuecollecting more verification log data, or in certain circumstances, setnew risk thresholds for the new software release (1205).

However, if the verification thresholds have been met (1224), then theAV software management system can verify the new software release forfully autonomous usage (i.e., by FAVs) (1225). For example, the AVsoftware management system can distribute the newly verified softwareversion to all FAVs, or a set of qualified FAVs operating throughout theautonomy grid. It is contemplated that not all FAVs will qualify for newsoftware releases due to their hardware-specific nature. For example, anolder model AV may not have an updated or state-of-the-art sensor towhich the new software release is tailored. The AV software managementsystem may then coordinate with the on-demand transport managementsystem described throughout the present disclosure to initiate usage ofthe newly verified software version by FAVs (1230).

FIG. 13 is a flow chart describing example methods of individualizedrisk regression-based vehicle matching by an on-demand transportationmanagement system, according to examples described herein. In certainexamples, the steps discussed with respect to FIG. 13 may flow fromblock (1115) of FIG. 11, and can therefore individualize risk assessmentper vehicle and/or driver given the vehicle's or the driver's currentstate. Furthermore, the below steps described with respect to FIG. 13may be performed by an example on-demand transportation managementsystem described in connection with FIG. 3. Referring to FIG. 13, theon-demand transport management system can monitor driver states foron-duty drivers of various on-demand transportation services (1300). Indoing so, the transport management system can track the time that thedrivers are online or on-duty (1302). In some aspects, the on-demandtransport system can further monitor sensor data from the driver'scomputing device, such as IMU data or image data that shows the driver'sface (e.g., from a forward facing camera of the driver's computingdevice) (1304).

In various examples, the transport management system can also monitoroperating states of AVs autonomously driving throughout the given region(1305). In doing so, the transport management system can identify whichsoftware version(s) the AV is currently running and which versions theAV has available (1307). In variations, the transport management systemcan also analyze log data streamed or otherwise transmitted from the AV(1309). As described herein, the log data can comprise telemetry data,diagnostics data, and/or sensor data from the AV's sensor suite (e.g.,LIDAR and image data). The log data can also include input datacorresponding to the AV control system's control inputs for the variouss control mechanisms of the AV (e.g., the braking, steering, andacceleration mechanisms). Accordingly, the transport management systemcan perform dynamic low-level monitoring of the AV's state and assess adegradation level for the AV.

In general, the transport management system can receive transportrequests from requesting users throughout a given region (1310). Eachtransport request can include or indicate a pick-up location (1312) anda destination for the requesting user or freight item (e.g., whentransporting goods) (1314). The transport management system candetermine a set of routes between the pick-up location and thedestination (1315). In certain examples, the transport management systemcan identify a most optimal route in terms of distance and or estimatedtime given current or expected traffic conditions. The transportmanagement system can also determine a current set of conditions and/ora predicted set of conditions along each route (1320). As describedherein, these conditions can include traffic conditions (1322), weatherconditions and/or lighting conditions (1321), a time of day (1323), roadconditions, any events occurring along the route (1324), such as publicgatherings, road constructions, parades, a mass egress event (e.g., whena concert or sports event ends), protests, and the like.

In certain implementations, the transport management system candetermine a candidate set of vehicles to service the transport request(1325). The transport management system can do so based on distance tothe pick-up location (1326) (e.g., within a mile), estimated time ofarrival to the pick-up location (1327) (e.g., within four minutes), andor estimated profitability for the vehicle (1328). The estimatedprofitability can be determined based on a variety of parameters, suchas whether the vehicle is an SDAV, FAV, or HDV, whether the vehiclerequires fuel or electric charge, the fuel or charge efficiency of thevehicle, the home location of the vehicle, the degradation level of thevehicle, how long the vehicle or the driver has been on duty, theservice type or vehicle type, which impacts the fare rates (e.g.,luxury, standard, economical, high capacity, mid-size, full-size,compact, or mini vehicle), and local demand for each vehicle's currentlocation. For example, the transport management system can monitortransport demand on a highly granular level (e.g., on the order of tensof meters), which enables the transport management system to induce orotherwise move vehicles towards highly localized areas of relativelyhigher demand. Accordingly, the transport management system can includea cost factor for each vehicle based on the transport demand within thelocal vicinity of that vehicle's current location. Accordingly, theestimated profit per vehicle can include an expected profit deductionattributable to moving the vehicle away from an area of higher relativedemand or, conversely an expected profit addition attributable to movingthe vehicle away from an area of lower relative demand.

In various examples, the transport management system can calculate anaggregate trip risk for each vehicle in the candidate set (1330). It iscontemplated that this risk calculation can highly individual based onthe driver state data (1332) and the current AV state (1334) (e.g., thedegradation level of the AV described herein. However, for certain AVshaving low or negligible degradation levels, the individual risk valuecan be the same, and in various examples, will converge to the generalaggregate risk determined by the risk regressor described herein.Accordingly, each vehicle in the candidate set may be associated with adistance and/or estimated time to the pick-up location, an estimatedprofitability for servicing the transport request, and an individualrisk value for servicing the transport request. Based on theseattributes, the transport management system may then select a mostoptimal vehicle from the candidate set to service the transport request(1335). Once the vehicle is selected, the transport management systemmay then transmit a transport invitation to the selected driver'scomputing device if the vehicle is an HDV, or a set of transportinstructions to the AV if the selected vehicle is an SDAV or FAV (1340).

FIG. 14 is a flow chart describing example methods of intelligentrouting of human drivers using fractional risk techniques describedthroughout the present disclosure. In certain examples, the belowprocesses described with respect to FIG. 14 can be performed by anexample on-demand transportation management system described inconnection with FIG. 3. Furthermore, the below steps of FIG. 14 may flowfrom block (1115) of FIG. 11, in which fractional risk values for pathsegments are generalized for human drivers. Referring to FIG. 14, theon-demand transport management system can receive transport requestsfrom requesting users throughout the given region (1400). For eachtransport request, the transport management system can determine anoptimal route from the pick-up location to the destination indicated inthe transport request (1410). The transport management system may thendetermine an aggregate risk for AVs over the optimal route (1410).

The transport management system may then determine whether all riskthresholds are exceeded for the AVs (1415). As provided herein, the riskthresholds of the SDAVs may be different from the risk thresholds forthe FAVs. Furthermore, the risk thresholds for each software version maybe different from each other. In still further examples, the samesoftware version may be attributable to different risk thresholdsdepending on such factors as whether the software version is beingexecuted by a SDAV versus an FAV, or whether the software version isbeing executed for verification mileage versus trip classifier training.If the aggregate risk does not exceed all thresholds (1417), then thetransport management system can perform a trip classification andvehicle selection process, described herein, in order to determine a setof candidate vehicles and select a most optimal vehicle to service thetransport request (1420). In doing so, the transport management systemcan select between fully autonomous vehicles executing verified softwareversions (1421), safety-driver autonomous vehicles executing eitherunverified, test software or verified software (1422), or purelyhuman-driven vehicles with a fulltime driver (1423).

However, if the aggregate risk for the optimal route exceeds all riskthresholds for AVs (1419), then the transport management system canfilter out all AVs from the candidate set of vehicles (1425), and selecta most optimal HDV or driver to service the transport request (1430). Indoing so, the transport management system can include factors such asdistance or time to the pick-up location (1431), the driver state (1432)(e.g., how long the driver has been on-duty or the driver's currentdriving characteristics), and/or the driver's historical safety rating(1433). The driver's safety rating may be determined from a storeddriver's profile, which can include passenger ratings for the driver,any incident reports, and the driver's personal accident or insurancehistory.

In various examples, the transport management system can aggregatefractional risk values over a plurality of route options for thetransport request to determine a least risky route (1435). Specifically,the transport management system can determine the aggregate risk valuesbased on a current set of conditions (1437). In variations, thetransport management system can further determine individual aggregaterisk calculations of the drivers over the least risky route option basedon the individual driver data described herein (1439). In furthervariations, the transport management system can determine individualrisk values for each of the drivers in the candidate set for each of theplurality of routes options, and select a most optimal driver (e.g., aleast risky driver/route combination) to service the transport request(1430). Once a most optimal driver is selected, the transport managementsystem can transmit a transport invitation and route data to theselected driver to facilitate the trip over the least risk route (1440).

FIG. 15 is a flow chart describing example methods of individualizedrouting, according to various examples. In certain examples, the belowprocesses described with respect to FIG. 15 can also be performed by anexample on-demand transportation management system in combination withan on-trip monitoring system described in connection with FIGS. 3 and 4.Referring to FIG. 15, the on-demand transport management system canmaintain driver logs for drivers operating throughout a given region(1500). These drivers can fulfill the on-demand transportation servicesfacilitated by the on-demand transportation management system, such aspassenger, food, package or general freight transport. Furthermore, thedriver logs can correspond to drivers of land vehicles (e.g., tractortrailers, cars, trucks, construction equipment, farming equipment, etc.)(1592), aerial vehicles (1591), marine vehicles (1593), remotelyoperated vehicles (1590), and/or hybrid vehicles encompassing aplurality of the foregoing (1594).

In various examples, the driver logs can store data indicating thedriving characteristics of the driver (1502). For example, the driverlog for a particular driver can indicate whether the driver has atendency towards late-braking, hard maneuver, hard acceleration, orotherwise indicate a safety level of the driver. The driver logs may beupdated dynamically, and can thus indicate live driver data (1503), suchas the current driving characteristics of the driver (e.g., via sensorresources from the driver's computing device or the vehicle beingoperated by the driver), how long the driver has currently been on-duty,and the current location, heading, and/or route plan of the driver. Incertain implementations, the driver logs can further store additionalprofile information, such as location preferences, the driver's safetyrating (1504), any specific incidences the driver has been involved in,and the like. In various examples, the on-demand transport system candetermine the general or current driving characteristics of the driverby receiving sensor data from the driver's computing device or vehiclesensors of the driver's vehicle (1595). For example, the on-demandtransport system can receiver IMU data (1596), image or video data(1597), and/or audio data (1598) from the driver's computing device orvehicle sensors to determine the driving characteristics of the driver.

According to various examples, the on-demand transportation managementsystem can identify or otherwise determine the destination of aparticular driver (1505). The destination can comprise a passenger orfreight drop-off location, a pick-up location, or a home destination forthe driver. The on-demand transportation management system can thendetermine a set of routes between the initial location (e.g., thedriver's current location) and the destination for the driver (1510).The on-demand transportation management system may then determine anindividualized risk value for the driver for each route to thedestination (1515). The transport system can determine theindividualized route based on the driver characteristics of the driver(1516), the live driver data (1517), and/or the characteristics of thedriver's vehicle, such as the vehicle's safety features, model, year,etc. (1518).

In various examples, the on-demand transport system can furtherdetermine a generalized risk value for each route, as described herein(1520). In still further examples, the on-demand transport system candetermine a failed ride risk probability for each route (1525). Forexample, the on-demand transport system can store historical dataindicating routes between a start location and a destination in which anunplanned detour (e.g., a missed turn or exit) has caused the driver oran AV to find a different route, or caused a routing resource executingon the driver's computing device or on-board computing resources of theAV to recalculate a new optimal route. Accordingly, the on-demandtransport system can leverage the historical failed ride data, orunplanned detour data, to determine the failed ride risk for each route(1527). Described in detail throughout the present disclosure areconcepts directed towards conditions based risk assessments. Accordingto examples, the failed ride or unplanned detour risk can also beconditions-based (e.g., either current conditions or predictedconditions), and thus the on-demand transport system can factor in thecurrent or predicted conditions into the failed ride or unplanned detourrisk probability (1529).

Accordingly, the on-demand transport system can determine an overallrisk value for each route as a weighted sum of at least a plurality ofthe individualized risk value, the generalized risk value, and thefailed ride or unplanned detour probability described herein (1530).Furthermore, it is contemplated that the on-demand transport system canperform each of the individualized risk, generalized risk, and failedride or unplanned detour probability computations at any time given thedriver's current location, current route to the destination, and anyother possible routes. Accordingly, at any given time, the on-demandtransport system can select an optimal route for the driver to thedestination (1535) based on the individualized risk (1536), thegeneralized risk (1537), and/or the weight risk probability (1538). Infurther examples, the on-demand transport system can then providerouting updates the computing device of the driver, to provideturn-by-turn directions for the optimal route to the driver (1540)(e.g., on a display screen of the driver's computing device).

FIG. 16 is a flow chart describing example methods of vehicle matchingbased on non-trip risk, according to examples. In certain examples, thebelow processes described with respect to FIG. 16 can also be performedby an example on-demand transportation management system described inconnection with FIG. 3. Referring to FIG. 16, the on-demand transportsystem can collect historical non-trip risk data for a given region(1600). As provided herein, non-trip risk data can comprise anyquantifiable risk external to the actual traversal of the vehicle fromthe initial location to the destination. In certain examples, ageneralized or individualized risk associated with the actual vehicles(e.g., HDVs versus SDAVs versus FAVs) in servicing an on-demandtransportation request can be quantified in terms of non-trip risk(1602). In further examples, the non-trip risk can be locational innature, for example, based on the pick-up location or start locationand/or the destination (1603). For example, the on-demand transportsystem can quantify a non-trip risk associated with a hospitaldestination. In still further examples, the on-demand transport systemcan quantify a non-trip risk value based on an event (e.g., a protest,concert, or sporting event) (1604).

According to various examples, the on-demand transport system canreceive transport requests in connection with an on-demandtransportation service (1605). As described herein, each transportrequest can comprise a start location (e.g., a passenger pick-uplocation) (1607) and a destination (1609). For each transport request,the on-demand transport system can determine a candidate set of vehiclesto service the transport request (1610). As further described herein,the candidate set of vehicles can include one or more HDVs (1611), SDAVs(1612), and/or FAVs (1613). The on-demand transport system can thendetermine a non-trip risk value for a trip corresponding to thetransport request (1615). In certain examples, the non-trip risk valuecan be individually calculated per vehicle (1617). For example, theon-demand transport system can determine the vehicle's safety rating orsafety features to factor in non-trip risk. The on-demand transportsystem can further factor in the degradation level of the vehicle, thesoftware being executed on the vehicle, and/or general risk associatedwith an HDV servicing the request as opposed to an SDAV or FAV.Additionally or alternatively, the on-demand transport system candetermine a generalized non-trip risk value based on the generalcharacteristics of the trip, as described herein (1619).

In various implementations, the on-demand transport system can infer thenon-trip risk based on the nature of the pick-up location and/or thedestination (1625). For example, a passenger going to the hospital maybenefit more from faster travel by an HDV as compared to an AV.Accordingly, the on-demand transport system can attribute a non-triprisk value to AVs in the candidate set of vehicles (e.g., to include ina weight sum risk calculation by the risk regression). The on-demandtransport system can further infer non-trip risk based on the freightbeing carried by the vehicle (1630). For example, a standard delivery ofnon-perishable goods can carry a lower non-trip risk than an emergencydelivery of medical supplies, or perishable food items. The on-demandtransport system can further infer non-trip risk based on an event, suchas a mass egress event at a pick-up location which can floodcomputational resources of an AV with dynamic objects, like pedestrians,to classify and predict (1635). The non-trip risk can further beinferred based on a current or predicted set of conditions, as describedherein (1640). Still further, the on-demand transport system can factorin non-trip risk based on a wait time by the requesting user (1645). Forexample, the user may benefit by waiting longer for a lower risk ride,or for a faster ride based on other non-trip risk factors.

Based at least in part on the non-trip risk value, the on-demandtransport system can select an optimal vehicle from the candidate set ofvehicles to service the transport request (1645). In doing so, theon-demand transport system may also factor in or otherwise optimizebetween individual and/or general risk per AV or driver, and/or failedride risk or unplanned detour risk. The selection can further be basedon filtering out vehicle types (1646) (e.g., between SDAVs, FAVs, andHDVs), and/or software versions being executed by the AVs (1647), asdetermined by the trip classifier(s) described herein. In furtherimplementations, the on-demand transport system can ultimately select anoptimal, lowest risk vehicle to service a given transport request basedon a weighted risk sum, as further described herein (1648).

FIG. 17 is a flow chart describing example methods of efficient fleetutilization in connection with an on-demand transport service, accordingto examples described herein. In certain examples, the below processesdescribed with respect to FIG. 17 can also be performed by an exampleon-demand transportation management system described in connection withFIG. 3. Referring to FIG. 17, the on-demand transportation managementsystem can collect fleet utilization data for a fleet of vehiclesoperating throughout a given region (1700). The fleet of vehicles cancomprise HDVs (1702), SDAVs (1703), and FAVs (1704). In general, theon-demand transport system can establish a set of selection prioritiesfor respective areas of the given region based on the fleet utilizationdata (1705). Thus, on a high level, the on-demand transport system canprioritize areas of an autonomy grid for on-demand transportationservices by AVs in general, SDAVs, FAV, or HDVs based on the fleetutilization data described below. Thereafter, the risk regression andtrip classification techniques described throughout the presentdisclosure can be implemented for servicing the on-demand transportationrequests.

In various examples, the on-demand transport system can establish theset of selection priorities dynamically based on a current or predictedset of conditions (1706). Furthermore, the fleet utilization data canindicate respective locations or areas of an autonomy grid at whichrides or on-demand trips are typically serviceable or not serviceable byAVs (1707). Such locations and areas can be time-sensitive as well asconditions sensitive. For example, an office building along an autonomygrid can be typically AV-serviceable at lunchtime, when workers travelshort distances for lunch, but AV-unserviceable in the evening, whenworkers travel lengthy and widely divergent paths to head home. Infurther examples, the on-demand transport system can establish thelocation-based selection priorities based on expected revenue betweenvehicle types (e.g., HDV, SDAV, and FAV), and/or software versionsexecuting on the AVs (1708). For example, the historical fleetutilization data can indicate areas having pick-up locations at whichAVs (SDAVs or FAVs) generate higher revenue than HDVs, and vice versa.Accordingly, the on-demand transport system can prioritize higherrevenue generating vehicle types based on expected revenue. In stillfurther examples, the selection priorities can be based on localizedcurrent or expected transportation demand (1709). For example, theon-demand transport system can diminish or reduce vehicle typeprioritizations when local demand increases in certain areas andlocations, in order to fulfill the increased demand.

As described herein, the on-demand transport system can manage anon-demand transportation service, such as a delivery or passengertransport service (1710). In various examples, the on-demand transportsystem can further match vehicles with requesting users based on theselection priorities, as described herein (1715). In certain variations,the on-demand transport system may dynamically determine the totalexpected revenue of the on-duty fleet at any given time (1720). Invarious examples, the on-demand transport system can determine the totalrevenue by aggregating localizes expected revenue and/or demand for thegiven region. In certain examples, the on-demand transport system canmove the vehicle supply to respective areas of the given region and/orautonomy grid in particular, based on the fleet utilization data and thedynamically determined expected revenue. For example, the on-demandtransport system can transmit transport commands to the AVs, providenotifications to the drivers, and the like. According to examples, theon-demand transport system can move the vehicle supply to higher demandareas (1737) and/or higher revenue or higher expected revenue areas(1739). As described, this active inducement of moving supply can bevehicle-type specific based on the selection priorities, or can begeneralized across areas.

Additionally or alternatively, the on-demand transport system candynamically adjust the size of the vehicle fleet based on expectedrevenue (1725). In various implementations, the on-demand transportsystem can do so by transmitting decommission and/or recommissioncommands to the AVs (SDAVs and FAVs) of the fleet (1730). For example,when the fleet is underutilized or has, cumulatively, relatively highwait times per match, the on-demand transport system can decommissionAVs accordingly. Conversely, if the fleet is over-utilized, theon-demand transport system can recommission AVs to fulfill the increaseddemand. In further implementations, the on-demand transport system canadjust the size of the vehicle fleet by way of transmitting post-tripinstructions to the AVs (1740). For example, the on-demand transportsystem can transmit instructions for an AV to return to a home location(1741), move to a high demand area (1742), execute an unverifiedsoftware version to log verification miles (1743), move to a higherutility area for the AV (1744), and the like. As provided herein, ahigher utility area can comprise an area within the given region wherethe individual AV is most optimally utilized (e.g., has lower risk,generates higher revenue, etc.).

Hardware Diagrams

FIG. 18 is a block diagram illustrating a computer system upon whichexample AV processing systems described herein may be implemented. Thecomputer system 1800 can be implemented using a number of processingresources 1810, which can comprise computer processing units (CPUs) 1811and field programmable gate arrays (FPGAs) 1813. In some aspects, anynumber of processors 1811 and/or FPGAs 1813 of the computer system 1800can be utilized as components of a neural network array 1812implementing a machine learning model and utilizing road network mapsstored in memory 1861 of the computer system 1800. In the context ofFIG. 5, various aspects and components of the AV control system 520 canbe implemented using one or more components of the computer system 1800shown in FIG. 18.

According to some examples, the computer system 1800 may be implementedwithin an autonomous vehicle (AV) with software and hardware resourcessuch as described with examples of FIG. 5. In an example shown, thecomputer system 1800 can be distributed spatially into various regionsof the AV, with various aspects integrated with other components of theAV itself. For example, the processing resources 1810 and/or memoryresources 1860 can be provided in a cargo space of the AV. The variousprocessing resources 1810 of the computer system 1800 can also executecontrol instructions 1862 using microprocessors 1811, FPGAs 1813, aneural network array 1812, or any combination of the foregoing.

In an example of FIG. 18, the computer system 1800 can include acommunication interface 1850 that can enable communications over anetwork 1880. In one implementation, the communication interface 1850can also provide a data bus or other local links to electro-mechanicalinterfaces of the vehicle, such as wireless or wired links to and fromcontrol mechanisms 1820 (e.g., via a control interface 1821), sensorsystems 1830, and can further provide a network link to a backendtransport management system or a remote teleassistance system(implemented on one or more datacenters) over one or more networks 1880.

The memory resources 1860 can include, for example, main memory 1861, aread-only memory (ROM) 1867, storage device, and cache resources. Themain memory 1861 of memory resources 1860 can include random accessmemory (RAM) 1868 or other dynamic storage device, for storinginformation and instructions which are executable by the processingresources 1810 of the computer system 1800. The processing resources1810 can execute instructions for processing information stored with themain memory 1861 of the memory resources 1860. The main memory 1861 canalso store temporary variables or other intermediate information whichcan be used during execution of instructions by the processing resources1810. The memory resources 1860 can also include ROM 1867 or otherstatic storage device for storing static information and instructionsfor the processing resources 1810. The memory resources 1860 can alsoinclude other forms of memory devices and components, such as a magneticdisk or optical disk, for purpose of storing information andinstructions for use by the processing resources 1810. The computersystem 1800 can further be implemented using any combination of volatileand/or non-volatile memory, such as flash memory, PROM, EPROM, EEPROM(e.g., storing firmware 1869), DRAM, cache resources, hard disk drives,and/or solid state drives.

The memory 1861 may also store localization maps 1864 in which theprocessing resources 1810—executing control instructions1862—continuously compare to sensor data 1832 from the various sensorsystems 1830 of the AV. Execution of the control instructions 1862 cancause the processing resources 1810 to generate control commands 1815 inorder to autonomously operate the AV's acceleration 1822, braking 1824,steering 1826, and signaling systems 1828 (collectively, the controlmechanisms 1820). Thus, in executing the control instructions 1862, theprocessing resources 1810 can receive sensor data 1832 from the sensorsystems 1830, dynamically compare the sensor data 1832 to a currentlocalization map 1864, and generate control commands 1815 for operativecontrol over the acceleration, steering, and braking of the AV along aparticular route plan based on transport instructions 1882 received froman on-demand transportation management system over the network 1880. Theprocessing resources 1810 may then transmit the control commands 1815 toone or more control interfaces 1821 of the control mechanisms 1820 toautonomously operate the AV along an autonomy route indicated in thetransport instructions 1882, as described throughout the presentdisclosure.

Furthermore, as described herein, the computer system 1800 may receivetransport instructions 1882 from an external on-demand transportmanagement system, instructing the computer system 1800 to rendezvouswith a requesting user to make a pick-up, and transport the user to adrop-off location. The processing resources 1810 can process thetransport instructions 1882 by generating a route plan and controlinstructions to execute the route plan to rendezvous with the requestinguser. In various examples, the transport instructions 1882 may betransmitted to the computer system 1800 based on location data 1818 ofthe computer system 1800 indicating that the AV is most optimallysituated to service a given transport request.

The computer system 1800 may further receive software versions 1884 fromthe AV software management systems described herein, and can selectivelyexecute the software versions 1884 based on transport instructions 1882received from the transportation management system. Furthermore, thecomputer system 1800 can transmit log data 1816 corresponding to atleast one of the sensor data 1832, the control commands 1815, and/ortelemetry and diagnostics data from the vehicle's electronic controlunit.

FIG. 19 is a hardware diagram illustrating a computer system upon whichexample backend software training, on-demand transport management, andon-trip monitoring systems described herein may be implemented. Acomputer system 1900 can be implemented on, for example, a server orcombination of servers. For example, the computer system 1900 may beimplemented as part of a network service for providing transportationservices. In the context of FIGS. 3-4, the AV software management system200, the on-demand transport management system 300, and the on-tripmonitoring system 400 may be implemented using one or more computersystems 1900 such as described by FIG. 19.

In one implementation, the computer system 1900 includes processingresources 1910, a main memory 1920, a read-only memory (ROM) 1930, astorage device 1940, and a communication interface 1950. The computersystem 1900 includes at least one processor 1910 for processinginformation stored in the main memory 1920, such as provided by a randomaccess memory (RAM) or other dynamic storage device, for storinginformation and instructions which are executable by the processor 1910.The main memory 1920 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by the processor 1910. The computer system 1900 may alsoinclude the ROM 1930 or other static storage device for storing staticinformation and instructions for the processor 1910. A storage device1940, such as a magnetic disk or optical disk, is provided for storinginformation and instructions.

The communication interface 1950 enables the computer system 1900 tocommunicate over one or more networks 1980 (e.g., cellular network)through use of the network link (wireless or wired). Using the networklink, the computer system 1900 can communicate with one or morecomputing devices, one or more servers, and/or one or more autonomousvehicles. The executable instructions in the memory 1920 can includerisk regression instructions 1922, which the computer system 1900 canexecute to determine fractional risk value for each path segment of anautonomy grid, and to aggregate these fractional risk values todetermine an overall risk value for a given trip, as describedthroughout the present disclosure.

The executable instructions stored in memory 1920 can also include tripclassification instructions 1924, which the computer system 1900 canexecute to establish respective sets of risk thresholds for softwarereleases and vehicles, and classify or filter trips based on theoutputted risk aggregates from the risk regression instructions 1922.The executable instructions stored in the memory 1920 can also includematching instructions 1926, which enable the computer system 1900 toreceive locations of AVs and human drivers operating throughout thegiven region, and match the AVs and human drivers to service transportrequests 1988 received from requesting users. For AVs, the computersystem 1900 may then transmit transport instructions 1952 identifyingthe pick-up location, route information, a software version to execute,and the like.

The executable instructions can further include trip monitoringinstructions 1932, which enable the computer system 1900 to determinemonitor AVs and human drivers operating throughout the region, andprovide suggested routes based on risk calculations for trip remainders,and/or generate AV commands 1954 instructing an AV to switch softwareversions or modes, or to drive to a service or home location forservicing or temporary decommissioning. Still further, the executableinstructions in memory 1920 can include software release verificationinstructions 1932, which enable the computer system 1900 to generatesoftware simulations for precertification and monitor AV logs forharmful events that impact the verification mileage for a given softwarerelease 1956. The software release verification instructions 1936 canfurther enable the computer system 1900 to establish verificationthresholds that, when met, enable the computer system 1900 to verify thesoftware release 1956 for distribution to fully autonomous vehicles(e.g., having level 4 or level 5 autonomous capability).

The processor 1910 is configured with software and/or other logic toperform one or more processes, steps and other functions described withimplementations, such as described with respect to FIGS. 1-13, andelsewhere in the present application. Examples described herein arerelated to the use of the computer system 1900 for implementing thetechniques described herein. According to one example, those techniquesare performed by the computer system 1900 in response to the processor1910 executing one or more sequences of one or more instructionscontained in the main memory 1920. Such instructions may be read intothe main memory 1920 from another machine-readable medium, such as thestorage device 1940. Execution of the sequences of instructionscontained in the main memory 1920 causes the processor 1910 to performthe process steps described herein. In alternative implementations,hard-wired circuitry may be used in place of or in combination withsoftware instructions to implement examples described herein. Thus, theexamples described are not limited to any specific combination ofhardware circuitry and software.

It is contemplated for examples described herein to extend to individualelements and concepts described herein, independently of other concepts,ideas or systems, as well as for examples to include combinations ofelements recited anywhere in this application. Although examples aredescribed in detail herein with reference to the accompanying drawings,it is to be understood that the concepts are not limited to thoseprecise examples. As such, many modifications and variations will beapparent to practitioners skilled in this art. Accordingly, it isintended that the scope of the concepts be defined by the followingclaims and their equivalents. Furthermore, it is contemplated that aparticular feature described either individually or as part of anexample can be combined with other individually described features, orparts of other examples, even if the other features and examples make nomention of the particular feature. Thus, the absence of describingcombinations should not preclude claiming rights to such combinations.

1.-20. (canceled)
 21. A computing system comprising: one or moreprocessors; and one or more memory resources storing instructions thatare executable by the one or more processors to cause the computingsystem to: receive a new unverified software version that relates tooperation of autonomous vehicles; generate a simulation forpre-certifying the new unverified software version, wherein thesimulation is generated using a previous verified software version andreal-world log data from at least one of the autonomous vehicles,wherein the real-world log data comprises at least one of: (i) recordedsensor data, (ii) telemetry data, or (iii) diagnostic data obtainedwhile the at least one autonomous vehicle implemented the previousverified software version; execute one or more implementations of thesimulation for the new unverified software version; and pre-certify thenew unverified software version for execution on the autonomous vehiclesbased on a result of the one or more implementations meeting apredetermined standard.
 22. The computing system of claim 21, whereinthe new unverified software version relates to operation of theautonomous vehicles within a given geographic region.
 23. The computingsystem of claim 21, wherein the simulation is also generated usingreal-world log data collected through a human driven vehicle.
 24. Thecomputing system of claim 21, wherein the instructions are executable bythe one or more processors to further cause the computing system to:adjust a simulation parameter of the simulation; and execute one or moreadditional implementations of the simulation.
 25. The computing systemof claim 24, wherein the simulation parameter is adjusted to include anadditional actor.
 26. The computing system of claim 25, wherein theadditional actor comprises at least one of: (i) a vehicle, or (ii) apedestrian.
 27. The computing system of claim 21, wherein executing thesimulation further comprises generating a simulation result.
 28. Thecomputing system of claim 27, wherein the instructions are executable bythe one or more processors to further cause the computing system to:verify, based on the simulation result, the new unverified softwareversion for at least one of: (i) further simulation, or (ii) real-worlduse.
 29. The computing system of claim 21, wherein the new unverifiedsoftware version is executable by computational resources of anautonomous vehicle to simulate autonomously operating control mechanismsof the autonomous vehicle based on a simulated sensor view.
 30. Thecomputing system of claim 21, wherein the instructions are executable bythe one or more processors to further cause the computing system to:compare data of the one or more implementations of the simulation forthe new unverified software version to data of one or moreimplementations of a simulation of a previously verified softwareversion.
 31. The computing system of claim 30, wherein to pre-certifythe new unverified software version for execution on the autonomousvehicles the computing system is caused to: determine that the data ofthe one or more implementations of the simulation for the new unverifiedsoftware version and the data of the one or more implementations of thesimulation of the previously verified software version are within aparticular range, and in response, pre-certify the new unverifiedsoftware version for distribution to the autonomous vehicles.
 32. Thecomputing system of claim 21, wherein the instructions are executable bythe one or more processors to further cause the computing system to:compare data of the one or more implementations of the simulation forthe new unverified software version to data of one or moreimplementations of a simulation of a human driven vehicle.
 33. Thecomputing system of claim 32, wherein to pre-certify the new unverifiedsoftware version for execution on the autonomous vehicles the computingsystem is caused to: determine that the data of the one or moreimplementations of the simulation for the new unverified softwareversion and the data of the one or more implementations of thesimulation of the human driven vehicle are within a particular range,and in response, pre-certify the new unverified software version fordistribution to the autonomous vehicles.
 34. The computing system ofclaim 21, wherein the autonomous vehicles comprise fully autonomousself-driving vehicles.
 35. A method comprising: receiving a newunverified software version that relates to operation of autonomousvehicles; generating a simulation for pre-certifying the new unverifiedsoftware version, wherein the simulation is generated using a previousverified software version and real-world log data from at least one ofthe autonomous vehicles, wherein the real-world log data comprises atleast one of: (i) recorded sensor data, (ii) telemetry data, or (iii)diagnostic data obtained while the at least one autonomous vehicleimplemented the previous verified software version; executing one ormore implementations of the simulation for the new unverified softwareversion; and pre-certifying the new unverified software version forexecution on the autonomous vehicles based on a result of the one ormore implementations meeting a predetermined standard.
 36. The method ofclaim 35, wherein executing one or more implementations of thesimulation for the new unverified software version comprises: executinga plurality of implementations of the simulation for the new unverifiedsoftware version, wherein after a first implementation of the simulationthe new unverified software version does not perform in accordance withthe predetermined standard, generating a targeted simulation report;debugging the new unverified software version; executing a secondimplementation of the simulation; and determining a performance of thenew unverified software version as compared to one or more previouslyverified software versions.
 37. The method of claim 35, furthercomprising: verifying the new unverified software version for at leastone of: (i) further simulation, or (ii) real-world use.
 38. The methodof claim 35, further comprising: adjust a simulation parameter of thesimulation, wherein the simulation parameter comprises an object withinthe simulation; and execute one or more additional implementations ofthe simulation.
 39. One or more non-transitory computer readable mediathat store instructions that are executable by one or more processors toperform operations comprising: receiving a new unverified softwareversion that relates to operation of autonomous vehicles; generating asimulation for pre-certifying the new unverified software version,wherein the simulation is generated using a previous verified softwareversion and real-world log data from at least one of the autonomousvehicles, wherein the real-world log data comprises at least one of: (i)recorded sensor data, (ii) telemetry data, or (iii) diagnostic dataobtained while the at least one autonomous vehicle implemented theprevious verified software version; executing one or moreimplementations of the simulation for the new unverified softwareversion; and pre-certifying the new unverified software version forexecution on the autonomous vehicles based on a result of the one ormore implementations meeting a predetermined standard.
 40. The one ormore non-transitory computer readable media of claim 39, whereinpre-certifying the new unverified software version comprises: comparingdata of the one or more implementations of the simulation for the newunverified software version to data of one or more implementations of asimulation of a human driven vehicle, and in response, pre-certifyingthe new unverified software version.